Contest: earliest written instance of economic thought

I was recently amazed to discover instances of computational social choice in The Nine Chapters on the Mathematical Art, the Chinese counterpart of Euclid's element, written by several generations of scholars from the 10th up to the 2nd century BCE.

The core issues in social choice theory is the question of fair allocation, or fair collective decision. Once an allocation rule is devised, it remains to make it applicable. One of the topics of computational social choice is precisely to build efficient algorithm to compute the solution of allocations rules given some inputs.

This is precisely the topic of chapter 3 and chapter 6 of The Nine Chapters on the Mathematical Art, which are entitled (resp.)  "Proportional Distribution" and "Fair Levies". The two fair division rules considered are the proportional division and the weighted proportional division rule. These rules are taken as given. They are not justified in any axiomatic way in the book, which would be the standard practice in modern social choice theory. Yet algorithm are proposed to apply them which in my views makes it a legitimate early instance of computational social choice ( because it answer the question  "given this allocation rule, how can we compute its solution for any possible inputs?" at a time when the answer to this question was far from obvious for the allocation rules considered).

Some example problems you will find in chapter 3 :

(All the examples below are taken form the excellent commented edition of the Nine chapters by Kangshen Shen, John N. Crossley, Hui Liu Oxford University Press, 1999. This edition contains great references to instances of these problem in written sources from other civilization, such as in Euclid's Elements)

[Problem 1]

  " Now Given five officials of different ranks : Dafu, Bugeng, Zanniao,
  Shangzao, and Gongshi jointly hunting 5 deers. Tell : how many does
  each get if [the deer are] distributed according to their ranks?"

[Problem 2]

  "Now given a cow, a horse and a sheep have eaten up the seedlings of
  someone's field. The landlord demands 5 duo of millet as compensation.
  The shepherd says : "My sheep eats hals as much as the horse." The
  horse owner says : "My horse eats half as much as a cow." The
  compensation is to be distributed according to the rates. Tell : how
  much should each repay?"

An example problems you will find in chapter 9 :

[Problem 1]

  "Now given the task of transporting tax millet is distributed among four counties. County A, 8 days from the tax bureau, has 10 000 households; County B, 10 days from the bureau, has 9500 households; County C, 13 days form the bureau, has 12350 households; County D, 20 days from the bureau, has 12 200 households. The total tax millet is 250000 hu needing 10000 carts. Assume the task is to be distributed in accordance with the distance from the bureau and the number of household. Tell : how much millet should each county transport? How many cart does each county employ?"


A classic example is to consider the question

  Why doesn't Tiger Woods mow his own lawn?

Woods is an excellent golf player. But he also may be excellent at mowing. Still, if he devotes time to mow the lawn he sacrifices time playing golf, in which he is much more productive.

A gardener who is skilled at mowing lawns but not at playing golf sacrifices less in order to mow, so even though Tiger Woods may be better at it (i.e. has an absolute advantage), the gardener has a comparative advantage. Both benefit if the gardener is paid for the time that he mows the lawn by Tiger Woods who in the time he saves can earn more by concentrating on golf.


This is more easily seen by writing out the budget constraints for periods 1 and 2 separately, and then eliminate the saving $s$.

In period 1, the agent spends $\left(1+T_1^c \right)\cdot c_1$ on consumption, and saves the rest, so
$$(1) \qquad \qquad s=y - \left(1+T_1^c \right)\cdot c_1.$$

In period 2, the agents lives on savings (together with interest income), i.e., consumes all the savings, so
$$(2) \qquad \qquad \left(1+T_2^c \right)\cdot c_2 = (1+r)s,$$ where we multiplied $s$ with $1+r$ because of the interest from period 1 to period 2.

Now putting the two equations together, we have
$$
\left(1+T_2^c \right)\cdot c_2 = (1+r)s= (1+r)( y - \left(1+T_1^c \right)\cdot c_1).
$$
Divide both sides by $(1+r)$ and rearranging the terms, you get the desired result.


Why do prices go up when the central bank increases money supply?

Price levels are not necessarily linked to interest rates, especially real rates.

Speaking strictly from an IS-LM standpoint, an expansionary monetary policy will increase the real money supply and depress interest rates, holding output constant. This will cause a rightward shift in the LM curve; holding money demand constant, the relative value of currency must fall.

Speaking practically, lower rates reduce the hurdle rate for investments and thus promote asset inflation. Further, the lower rates will also find their way into consumer credit, increasing purchasing power among consumers and thus driving up prices of consumer goods. Mind that the former precedes the latter by a good couple years.

Hope that answered your question.


Are expensive cities economically threatened by an inability to house low-income workers?

Something missing from more traditional analyses is that a significant number of people may move to a new (expensive) location, take a job that pays a wage insufficient to cover living expenses, and burn through their savings until they're forced to relocate.  This happens quite often in "fashionable" locations such as San Francisco.

This is actually a strong argument in favor of a higher minimum wage in such places, even taking a "hard-line" position that such laws only result in the loss of low-paying jobs — because such jobs are a "trap" that lures people (with savings) into an unsustainable situation in which they are exploited until they run out of savings, at which point they are replaced by somebody else with savings to exploit.  Eliminating all jobs that pay wages insufficient to actually live (on an ongoing basis) in expensive markets guarantees that such "exploit the newcomers" economies cannot persist for very long.


Retailer Price Setting for Existing Inventory in the Event of an Increase in Price Paid for the Good

At the time period in which you buy the new retail goods, the cost of having bought the old inventory goods is now a sunk cost. Your pricing decision I don't think should be based on the price of the old inventory. The new point where marginal cost equals marginal benefit (assuming competitiveness) is now the price of the new stock (more expensive).

Without competition though, the result will probably change, since the quantity you supply will now matter in affecting demand.


What does increase or decrease in marginal utility mean in terms of quantity demanded?

Typically, we have concave, increasing utility functions. So that means, the more you consume of a good x the more utility you have. However, the marginal utility (the first derivative of the utility function) is decreasing in consumption. So each additional unit of consumption has less additional value than the one before.
A simple example would be food. If you're starving, that first bite of food has the highest utility. But one you get full, additional bites will only have a little additional utility. So the more you consume, the less satisfaction from each additional bite.
In this sense, a higher marginal utility, means a lower level of consumption. That could mean a higher demand.


Basic New Keynesian Model - Price and Wage Level after shock

Try to give a look at what happens to inflation's IRF. If it stays positive for the whole horizon of the IRF then simply prices have increased over time at the inflation rate. I guess that any non-degenerate price level (nominal!) is compatible with such model structure, as its system is written down in growth rates, as that's what loglinearised variables are.


What is the Relationship between expected return and risk of corporate bonds?

If interest rates decrease, people have less alternatives to lend at higher rates. Thus people are willing to accept lower yields. Hence, bonds prices go up (and yields go down).

If the default risk increases, people want higher yields to make up for that risk. Hence, bonds prices go down (and yields go up).


Max Utility Function and Finding associated demand curve

From your formula for $x$ when $y=0$, you should be able to find $U$ in terms of $M$ and $p_x$ when $y=0$.  Similarly, $U$ in terms of $M$ and $p_y$ when $x=0$.  The key then is to find the critical price ratio at which, to maximise $U$, the switch needs to occur from $y=0$ to $x=0$.

Can you take it from there?


Am I using quantity and price correctly in trying to calculate price elasticity of supply?

It does not really mater, as you will get the same numerical values eventually. You may choose whatever is more convenient in each particular situation. Convention is, however, to replase $q$ with $q(p)$ to derive a formula for elasticity as a function of the price only.


The referenced link from money.stackexchange.com with the top accepted answer is incorrect.  It states:

"Coins are assets because its the actual money. Notes are liabilities because the Federal Reserve is obligated to pay money on these notes."

The Federal reserve is not obligated to pay money on the monetary base it creates and there is no objective financial difference between coins being real money and federal reserve notes being real money.  They are both real money for all practical purposes.

Let me take a stab at the initial question.

Yes, Federal Reserve Notes are a liability to the Federal Reserve (as are electronic federal reserve dollars).  It doesn't really need to be done like this.  It's actually a holdover from when the US was on a partial gold standard.  The central bank would hold gold deposits and in turn issue notes (dollar bills) which were redemption slips for those gold deposits.  Because the central bank had to redeem dollar bills with gold, this is why it was classified as a liability.  The US went off the gold standard twice (once under FDR to the public and later under Nixon for foriegners).  When this happened, dollars were no longer true liabilities.  But this history is why you see federal reserve notes (and federal reserve deposits which are electronic dollars) still classified as liabilities.  And coinage is considered an asset to the Fed which they purchase from the treasury.  You will still see some gold as an asset on the Fed balancesheet as a holdover from these times.

The Federal Reserve does not have a monopoly on creating the monetary base.  The treasury can also create base money by creating coins and US Notes (which are not Federal Reserve notes).

The treasury does not consider the coin as a liability because it truly is not one (just as the dollar bill in a modern sense is not one to the Fed).

Looking at a balance sheet, you could almost argue that base money is a liability because it appears to be balanced by assets on the balance sheet.  But this is not the case.  It is an accounting illusion. If the Fed were to withdraw all their "liabilities" there wouldn't be any money in the system (outside of coins and notes of course).

The reference to the obligation on Federal Reserve Notes is an antiquated holdover from the gold standard era.  It is quite meaningless now.  The Fed will exchange if demanded electronic dollars for paper dollars, vice versa or will buy/sell coinage as required by the banks.

Coinage is not an obligation to the treasury, neither are US notes.  The treasury will certainly repair damaged coins, but that is not to say that the coins themselves are true liabilities or obligations.


Where are the payments for export in the balance of payment?

The physical part of trade in goods and services is recorded in the Current Account and the payment (with financial assets) part of the transaction is recorded in the Financial Account. The Balance of Payments should be 0 after you sum up the Current Account balance, the Capital Account and the Financial Account balances. The trade in goods and services is recorded in Current Accounts. For instance, exports are credited and imports are debited.


Yes, there is an income effect, as the individual needs to work less in order to obtain the same amount in the numerator (relative to before the increase in exchange rate). The assumption is that the substitution effect dominates the income effect here, regardless of whether the individual is a borrower or a lender.


When integrals look different than what pops into your head, often the reason is integration by parts. For your example note that
$$\int_R^1 (\theta -R) g(\theta) d \theta + \int_R^1 G(\theta) d \theta = (1-R) - 0,$$
where the right-hand side is equivalent to $\int^1_R 1 d\theta$.
Hence, the two expressions you consider are equivalent.
It's of the form
$$\int u(x) v'(x) dx + \int u'(x) v(x) dx = \int u(x) v(x) dx.$$


Explanation of differential equation for Price Elasticity of Demand

Consider a demand equation,
$$Q=\alpha\ +\beta\ P$$

If you were to just use,

$$e_{{\langle p\rangle }} = {\frac  {{\mathrm  {d}}Q}{{\mathrm  {d}}P}}$$

Then the result is only the differentiation with respect to $P$. This doesn't give you the information that you are looking for because you haven't specified the point at which you are measuring elasticity. To do this the second portion of the equation is necessary. Suppose you want to know the elasticity at $P=\$8$. By plugging $\$8$ into the demand equation you can calculate the quantity demanded at $P=\$8$.

Personally, I have to think of this in two parts, (1) the differentiation with respect to $P$ (2) multiplied by the "spot" of the demand curve that I am examining, ${\frac PQ}$.

$e_{{\langle p\rangle }}={\frac  {{\mathrm  {d}}Q}{{\mathrm  {d}}P}}* {\frac PQ}$

edit

The number that you calculate is the percent change of a 1% increase in price. For example, if $e=-0.3$, you can say that a 1% increase in price should cause a 0.3% drop in the quantity demanded.


Emergence of notion "primitive business" in the era of Industry 4.0 and globalization

There surely is research about the complement(s) of the (low tech) "primitive businesses", i.e. the high-tech and/or innovative businesses.
The US CES for instance publishes a BDS-HT (Business
Dynamics Statistics - High Tech) report. You can compare these with the core BDS data and get an idea how the high-tech sector differs. The US CES method of high-tech business classification is based on the number of STEM employees.

Ultimately, High Tech industries account for about 4% of firms and 6% of employment.

So there are a lot "primitive businesses" by this standard, even in the US.
A different method is used by CES for finding/reporting "innovative firms", BDS-IF. This is based on patents (BDS-PF) and trademarks.
I'm guessing the German Industrie 4.0 that motivated your question is probably similar to BDS-HT, but I don't know their exact methodology.


trading security volume that affects price

There are countless models, from different perspectives (theory/econometric/etc.) on price impact of trade volume. The seminal model in market microstructure---the Kyle model (85 Econometrica) addresses this very issue. There are many descendents of this model.

To demand a "formula" is a little simplistic. The usual framework for these models, if micro-founded, is strategic. The price impact is a property of the resulting game-theoretic equilibrium. In the Kyle model, one has

$$
\Delta p = \lambda\cdot Q
$$

where $\Delta p$ is the price adjustment of market maker when the incoming total order flow is (only market orders are considered) $Q$. The coefficient $\lambda$ (Kyle's lambda) is a measure of price impact, and is determined by the severity of the adverse selection problem faced by the market maker.


Constant Elasticity of Substitution (non-special cases)

The exponents do not cancel each other out. E.g.,
$$
(a^2+b^2)^{1/2} \neq a + b
$$
because
$$
(a^2+b^2)^{1/2} \neq (a^2 + 2ab + b^2)^{1/2}.
$$


Is there any difference between using LRAC and LRATC in a long-run market structure diagram?

It is one and the same thing. Notation might be different.


Visual Representation of mixed strategy profile using a simplex in $R^3$

I'm not sure what you're asking is possible.

Start with the intuition from the case with 2 pure strategies. A player's mixed strategy is a 2-vector $\sigma_i(p)=(p,1-p)$, which can be visualized on a 1-dimensional simplex (or simply a line). Therefore, a profile of two mixed strategies can be represented as a point in a 2-dimentional plane, because this profile is controlled by two parameters (say $p$ for player 1 and $q$ for player 2) like the following

With 3 pure strategies, a mixed strategy is a 3-vector $\sigma_i(p_1,p_2)=(p_1,p_2,1-p_1-p_2)$. It is controlled by two parameters, and so we need a 2-dimensional simplex to represent one (mixed) strategy. For a profile of two mixed strategies, we need two 2-dimensional simplices, or planes. Naturally they should be perpendicular to each other just like the two 1-dimensional simplices are in the 1-dimensional case, although this is not a hard requirement. The difficult part is that we now have four controlling parameters, e.g. $p_1,p_2$ for player 1 and $q_1,q_2$ for player 2. So the strategy profile lives in a 4-dimensional space, which is impossible to visualize.

The following is a failed example. Player 1's mixed strategy $\sigma_1(\hat p_1,\hat p_2)$ lives in the blue simplex and Player 2's mixed strategy $\sigma_2(\hat q_1,\hat q_2)$ lives in the orange simplex. The two simplices share the vertical axis, but this is not necessary. As the two red dashed lines show, it's not always possible to find an "intersection" of the two strategies (the two lines have different heights).


Solving problem for optimal price (maximize profit) *attempt inside*

Start with setting up profit equation:
$$\max_p \pi = (p-c)D(q)$$
Then substitute demand in:
$$ \pi = (p-c)(B-bp)$$
Take derivative of profit and equate it to 0:
$$\frac{d \pi}{dp}=B-2pb + cb=0$$
Now solve for optimum price:
$$p^*= \frac{B+cb}{2b}$$
There is also alternative way how to get to the same solution that uses elasticity of demand.
There is well known monopoly optimal pricing rule (see Peitz and Belleflamme Industrial Organization: Markets and Strategies) says that optimal monopoly prices are given by:
$$\frac{p-mc}{p}=-\frac{1}{El}$$
In your case marginal costs (mc) are $c$, and your elasticity of demand is actually:
$$EL= -\frac{bp}{q}= -\frac{bp}{B-bp}$$
In your attempt you forgot to substitute for Q in your elasticity.
Plugging these into optimal pricing rule gives us:
$$\frac{p-c}{p}=-\frac{1}{\left( -\frac{bp}{B-bp} \right)    }$$
Which when solved for $p$ gives us again:
$$p^*= \frac{B+cb}{2b}$$
Both approaches give the same solution - the first one is quicker in this case in my opinion.
Now regarding what we say about price and parameter $b$, we can just rearrange the optimal price formula as:
$$p^*= \frac{B}{2b}+\frac{c}{2}$$
So clearly we can see that when $b$ parameter increases optimum price decreases.
We can also prove it more formally by inspecting derivative of $p^*$ with respect to $b$ which gives us:
$$ \frac{d p^*}{d b} = -\frac{B}{2b^2}$$
Since the derivative is negative we confirmed that the price varies negatively with $b$.
Next, per request in comment on what is elasticity at equilibrium price we can calculate that just by substituting the equilibrium price into elasticity which we already calculated above:
$$EL(p^*)= -\frac{b p^* }{B-bp^*}=   -\frac{b \left( \frac{B+cb}{2b} \right)}{B-b \left( \frac{B+cb}{2b} \right)} \\= - \frac{B+bc}{B-bc}$$
The last expression will be bigger than 1 in absolute value because the numerator will be larger than the denominator ($B+cb>B-bc$) and absolute value will make the value positive.


How to engineer the economic system, so that an individuals self interest leads to fulfilling collective interest

Well, there's mechanism design. We use game theory to study what people do and then reverse it and ask ourselfs what set of rules induce people to rationally choose to do what the mechanism designer wants them to do.

In an auction, we charge the second highest price to the winner, that's a Vickrey-Clark-Groves mechanism. In voting, one might want to apply the Goeree-Zhang mechanism, for example.


Does analysis at two points in time count as 'longitudinal'?

Yes,  A longitudinal study involves repeated observations of the same variables (e.g., people) over short or long periods of time.   You can have more than two points in time for your project.  It really depends on your study's converge (time periods under analysis).


What would the indifference curve of min{√x,y} looks like?


In auction theory, why is my own valuation a random variable?

In most of the literature I have read on private value auctions you do actually know your own valuation in the auction, it is your private information. It has a distribution from the point of view of others, who can only guess at your valuation.

Another interpretation would be that your valuation is actually random before getting detailed information on the object you are bidding on. How much would you pay for this bottle of wine that I have here on my shelf? Oh, it depends on the wine? Then without knowing the exact type of wine and assuming it has some random distribution your valuation is also a random variable. Once you learn the exact type of the wine you learn your own 'type', which is modeled as getting a private signal about your valuation.

In case of noisy observation your valuation can actually be random. E.g. I tell you the type of wine but you have never had it before. Or in case of gas companies, they have an inexact estimate about the yield of a gas field. Their 'real' valuation depends on the 'real' yield, without knowing that they can only bid based on some expected values.


Trying to track down the earliest usage of rivalry, tangibility, excludability, and durability in economics

You can try Google Books Ngram Viewer. On the bottom of the graph, there is a Search in Google Books giving access to the references.


Estimating production functions with time series data

How do we develop concise production functions from a set of equations
  like these?

It goes the other way around.

VAR's are  a special group of models for time series data, developed mainly for atheoretical forecasting purposes. In a VAR approach we do not assume some structural economic model, we just want to use the past of a variable and its interelations with other variables, to form a good quality predictor (and it is for this reason that when they were first introduced and propagated by Sims,  they created a lot of controversy).

In general, estimating a production function of a single firm using time-series data does not alter the econometric approach described in the answer to your other question, only here the assumption we make is that the unknown parameters refer to a single firm (and not to many firms), and so stay constant across time (and not across firms on the same time period.

A new econometric issue is that with time series data we are forced to deal with the aspect of serial correlation (in a cross-sectional setting, "serial" correlation makes no sense because we can permutate the index without consequences). Serial correlation may emerge in two ways: a) In the regressors  b) in the error term.

A new economic issue in a production function setting is the intertemporal change in Total Factor Productivity.


Why do expectations of inflation decrease SRAS (Short Run Aggregate supply)?

That’s because you are confusing inflation expectation (increases in prices in the future) with just inflation (i.e. increasing prices now). If aggregate prices increase quantity produced would increase. However, an expectation of price increases in future shifts supply curve to the left as businesses will prefer just to hold on the inventory today and sell it in the future at higher price. If you have an apple and you get option of selling it today for 10e or week after for 50e you will probably prefer the latter (ignoring potential behavioral effects such as hyperbolic discounting and assuming rationality). This will shift the supply of apples in the short run to the left.

Similarly when it comes to aggregate demand higher inflation expectations would actually increase demand, because if you expect prices to be high in the future you want to buy stuff you want now.

You should be very careful not to confuse inflation expectations and inflation they work completely differently.


Subgame Perfect Equilibrium in Baye, Shin (1999)

True to the saying that a picture is worth a thousand words, let's plot the profit function under the Stackelberg game and see what we're dealing with.

You correctly observed (or copied from the Baye, Shin paper) that the reaction function of player 2 is:

$$
R(x_1)=\frac{2\mathit{x_1}-\sqrt{2}\,\sqrt{2\mathit{x_1}-{{\mathit{x_1}}^{2}}}}{2\mathit{x_1}-2}\
$$

Inserting this reaction function in the profit function of player 1 gives us a complicated expression:

$$
\pi(x_1)=-\frac{{{2}^{\frac{3}{2}}}\,\sqrt{2-\mathit{x_1}}\,{{\mathit{x_1}}^{2}}-2{{\mathit{x_1}}^{\frac{3}{2}}}-3\sqrt{2}\,\sqrt{2-\mathit{x_1}}\,\mathit{x_1}+4\sqrt{\mathit{x_1}}}{{{2}^{\frac{3}{2}}}\,\sqrt{2-\mathit{x_1}}\,\left( \mathit{x_1}-1\right) }
$$

(Perhaps more advanced symbolic math programs can further simplify this, mine won't). In any case this is  a function of one variable $\mathit{x_1}$, and can be plotted. For completeness I have inserted the original equilibrium as well.

As you can see the original NE is no longer a local maximum, and player 1 can increase its profits by increasing their effort.


Multivariate implicit differentiation (Samuelson, 1948)

We start from the equation:
$$
Y = C(Y - \overline{W}) + \overline{I} + \overline{G}
$$
First, we totally differentiate with respect to $(-\overline{W})$ (taking into account that $Y$ is a function of $\overline{W}$, $\overline{I}$ and $\overline{G}$)
$$
dY = C'(Y - \overline{W})dY + C'(Y - \overline{W}) d(-\overline{W}).
$$
Bringing the $dY$ terms on one side gives:
$$
(1 - C'(Y - \overline{W}))dY = C'(Y - \overline{W}) d(-\overline{W}),\\
\to \frac{dY}{d(-\overline{W})} = \frac{C'(Y - \overline{W})}{1 - C'(Y - \overline{W})}
$$
Next, we totally differentiate with respect to $\overline{G}$:
$$
dY = C'(Y - \overline{W})dY + d \overline{G}
$$
Again gathering terms gives:
$$
(1 - C'(Y - \overline{W})) dY = d \overline{G}\\
\to \frac{dY}{d \overline{G}} = \frac{1}{1 - C'(Y - \overline{W})}
$$
Therefore:
$$
\begin{align*}
\frac{dY}{d \overline{G}} - 1 &= \frac{1}{1 - C'(Y - \overline{W})} - 1,\\
&=\frac{1 - 1 +C'(Y - \overline{W})}{1 - C'(Y - \overline{W})},\\
& = \frac{C'(Y - \overline{W})}{1 - C'(Y - \overline{W})},\\
&= \frac{dY}{d(- \overline{W})}
\end{align*}
$$


Why does US equities valuation have the downward pressure on USD?

I think what your quote is trying to say is: Investors have bid up US stocks to the point where their valuation ratios (e.g. pe) are high relative to other countries. This could lead investors to rebalance in favor of other stocks. Hence the downward pressure on the dollar.
Whether or not that argument makes sense is another questions (about which I have my doubts).


This seems to be driven, in large part, by the recent economic downturn in China, which is both a major source and a major destination for trade in goods.

A Bloomberg report has this to say:

  Shipbuilders, container lines, and port operators feasted on China’s rise and the global resources boom. Now they’re among the biggest victims of the country’s slowdown and the worldwide decline in demand for oil rigs and other gear amid the oil price plunge. China’s exports fell 1.8 percent in 2015, while its imports tumbled 13.2 percent.

Moreover, there seems to be an overcapcity in the shipping industry thanks to the fact that shipbuilders ordered many new fuel-efficient ships in a bet that oil prices would remain high. From the same article:

  Just a few years ago, as the global economy improved and oil prices rose, many companies ordered more fuel-efficient ships. There were more than 1,200 orders for bulk carriers that transport iron ore, coal, and grain in 2013, compared with just 250 last year, according to Clarksons. Many of the ships ordered are now in operation, says Tim Huxley, chief executive officer of Wah Kwong Maritime Transport Holdings, a Hong Kong-based owner of bulk carriers and tankers. “You have a massive oversupply,” he says.

Any standard economic model is going to predict that an oversupply of ships implies a reduction in the price of shipping, which hits the profits of shipping companies.


One way to think about this is that money is put in circulation when the central bank buys assets from the citizens of a country. Sometimes this is really the main thing going on, for example, to perform QE the Fed bought all kinds of securities from financial markets. When the fed buys these securities it pays banks by increasing the deposit balances of those agents selling the asset.

Another simple way that a central bank "buys assets" is when it gives a loan to a commercial bank. In the process of given the loan, the central bank will give physical cash to the commercial bank and keep the loan in its books as an asset.

This could also happen in an accounting sense but not in an economically meaningful sense, for example when a central bank "buys" bonds issued by the government of its country. The government might never pay or the bonds might loose most of their value if inflation increases interest rates for example, so its a kind of fake purchase.

Finally, economists talk about a 'helicopter drop', where the central bank would print money and then just through it out fro a helicopter, hoping it is evenly distributed among the population... but that's mostly just an imaginary scenario.

[BUT, NOTE: This is a caricature of how physical currency can be printed by a bank and shipped out. Quantitatively, an important part of money creating takes place within the banking system, as banks receive deposits from clients and give out loans, physical money gets "multiplied".]


When Does a Shift to the Right Happen in the Demand and Supply Curves Simultaneously?

To answer your first question: No, it shifts out only the demand curve but not the supply curve. However, the quantity supplied increases.

To understand why the advertisement does only shift out the demand curve but not the supply, you have to understand two important concepts:

(1) Shift of the demand curve = Increase of demand;
(2) Movement along the demand curve = Increase of quantity demanded.

Similarly with supply:

(1) Shift of supply curve = Increase of supply;
(2) Movement along the supply curve = Increase of quantity supplied.

Now what happens, if you advertise for pizza? The demand as well as the quantity supplied increase. To see this, consider the following. More people demand pizza so the demand curve shift out and the demand increases. At the same time, the supply curve does not move. BUT, the outwards shift of the demand curve intersects the supply curve further to the right, and hence, the quantity supplied increases.

Shifts of the demand curve can be caused, for example, when people's income increases or decreases. Demand increases usually increase the quantity supplied.

Shifts of the supply curve can be caused, for example, through the the use of better technology that lowers producers marginal production costs. Supply increase usually increases the quantity demanded.

Increases or decreases of quantity demanded or quantity supplied can be caused through price changes.

A simultaneous increase of demand and supply can be cause if consumers get a higher income and firms get a better technology at the same time.


Understanding Vernon Smith's 1962 "An Experimental Study of Competitive Market Behavior"

I suspect that the reason for that result is the way how you modeled the situation is by having the prices to be offered at random from a sample of numbers in uniform fashion (unless I am misreading the code) but this is unrealistic.
As mentioned in the paper Smith's 1962:

each sequence of experiments was conducted over sequences five to ten minutes long

So the students had only 5-10 minutes to actually conclude the trades. Students knew their own valuation of the item and they wanted to get it.
In such situation it is not in students interest to just choose random price from the sample of prices. Rather students will be choosing from some distribution that will have more mass closer to the their own valuation.
My prediction is that if instead of random sampling of prices from the array you will let the program generate numbers from some distribution that has more mass closer to the right price you will see less volatility in the data.
For example, instead of using random.choice(price_deltas) you could use random.choice(price_deltas, p=weights) that allows you to assign weights to numbers so some are more likely than others.

EDIT:
You could use the weight approach above but I also discovered much simpler one. If you are just fine assuming prices can be continuous (which from point of economic theory is fine 0.01c is really just practical limit in real life), then what you can do is skip the creation of deltas at the start:
import numpy as np
import matplotlib.pyplot as plt
buyers = [3.25 - 0.25*n for n in range(0, 11)]
sellers = [3.25 - 0.25*n for n in range(0, 11)]

Next what you can do is to replace random.choice() with some distribution that has most mass at zero - like the exponential distribution. In python this can be done using random.exponential(), the code would thus change only minimally, and will generate deltas that are closer to the actual valuation of the traders:
trading_prices = []
trading_counts = []

for _ in range(5):
    buyers_still_in_market = list(range(len(buyers)))
    sellers_still_in_market = list(range(len(sellers)))

    transactions = []

    for _ in range(1000):
        # First, a random buyer offers to buy at some price below their number on
        # their card
        b = np.random.choice(buyers_still_in_market)
        delta = np.random.exponential(400)
        buyer_price = max(0, buyers[b] - abs(delta))
        s = np.random.choice(sellers_still_in_market)
        if buyer_price >= sellers[s]:
            transactions.append((b, buyers[b], s, sellers[s], buyer_price))
            buyers_still_in_market.remove(b)
            sellers_still_in_market.remove(s)
            trading_prices.append(buyer_price)

        # Then, a random seller offers to sell at some price above their number on
        # their card. #0.65 - for log normal
        s = np.random.choice(sellers_still_in_market)
        delta = np.random.exponential(400)
        seller_price = sellers[s] + abs(delta)
        b = np.random.choice(buyers_still_in_market)
        if seller_price <= buyers[b]:
            transactions.append((b, buyers[b], s, sellers[s], seller_price))
            buyers_still_in_market.remove(b)
            sellers_still_in_market.remove(s)
            trading_prices.append(seller_price)

    trading_counts.append(len(trading_prices))

    print(len(transactions), "transactions")

plt.plot(list(range(len(trading_prices))), trading_prices)
plt.ylim(0, 4.00)
for x in trading_counts:
    plt.axvline(x=x-1, ymin=0, ymax=4.0, color='red')
    print("line drawn at x =", x)
plt.show()

print(trading_prices)

This new code produces results that are much less 'jagged'. Here are some examples, of behavior of price with the exponential distribution (possibly different types of distribution would yield better result but its beyond scope of SE answer to examine that). For example, here are 8 graphs generated when the prices depend on exponential distribution:


Unsure how to go about computing GDP for the following problem

Generic growth formula:

$$\frac{V_t}{V_{t_0}} = (1 + r)^{t-t_0}$$

Solving for $r$ will give you a growth rate in terms of the units of $t$ and $t_0$.  In your example, that's years.

You currently have a growth rate and the value in 1997.  Projected value in 2012 would be

$$V_{2012} = V_{1997} \cdot (1 + r)^{2012-1997} = V_{1997} \cdot (1 + r)^{15}$$

This assumes that $r$ is the decimal (not percentage) average growth rate that you calculated.

I'll leave it up to you whether your original calculations of the growth rate are consistent with this method of calculating the growth rate.  If not, you may want to recalculate it.

The formula that I gave is a generic one.  While you may be applying it to GDP, this will work for the growth rate of any variable.  That's why I used $V$ for value rather than a variable more commonly recognized as representing GDP.


My reading is yes, it is procyclical in levels and margins.

  This paper uses industry and firm data to look at price cost mark-ups
  and firm profit margins in U.K. manufacturing and services. In
  particular it examines how they behave over the business cycle. It has
  two main findings. First, the estimated average mark-ups and the
  profit margin results both suggest that there is imperfect competition
  in manufacturing and services. Second, mark-ups are pro-cyclical, as
  are profit margins even after allowing for movements in their standard
  determinants. This suggests that price pressures may increase during
  recovery periods and decrease during recessions. One possible
  explanation for this is Kreps and Scheinkman's argument that the
  pro-cyclicality of capacity constraints means that firms move between
  Cournot and Bertrand competition over the cycle. The finding that
  mark-ups are pro-cyclical also raises doubts about macroeconomic
  models that assume that demand shocks may affect employment via
  counter-cyclical mark-ups.

The Cyclicality of Mark-Ups and Profit Margins: Some Evidence for Manufacturing and Services

  In this paper, we assess the cyclicality of mark-ups and profit
  margins within the United Kingdom, at both the aggregate and industry
  level. We find that the private sector labour share moves
  countercyclically, suggesting that the aggregate mark-up moves
  procyclically. This result survives when we consider more
  sophisticated measures of the mark-up. And this result is also
  supported by industry-level data. We find that the aggregate market
  sector profit share moves procyclically and that the cyclical
  behaviour of profit margins is largely homogenous across industries.
  Nevertheless, there is some evidence that margins moved against the
  cycle in the late 1990s, starting to fall in 1997, whereas GDP growth
  did not peak until 2000. In tandem with these cyclical movements, we
  also find that the market sector profit share has trended upwards
  since 1970, in contrast to the aggregate mark-up, which fell over the
  same period.

The Cyclicality of Mark-Ups and Profit Margins for the United Kingdom: Some New Evidence

  Modeling cash flows separately from aggregate consumption is crucial
  since corporate cash flows have historically been far more volatile
  and sensitive to economic shocks than has aggregate consumption. For
  example, corporate earnings have been more than ten times as volatile
  as consumption growth during the post-war period. Similarly, while
  aggregate consumption declined nearly 10 percent during the early
  stages of the Great Depression, aggregate corporate earnings were
  completely obliterated as they fell more than 103 percent. In addition
  to being more volatile, corporate cash flows are also highly
  correlated with aggregate consumption because of their strong
  procyclical behavior. To provide specifics, during the 1929 to 2001
  period the volatility of earnings growth was 29.5 percent, while the
  correlation between per capita real consumption and earning growth was
  68.7 percent.

  Intuitively, the reason for the extreme volatility and procyclicality
  of corporate earnings is that stockholders are residual claimants to
  corporate cash flows. Thus, the compensation of workers is a senior
  claim to cash flows. In other words, labor contracts provide workers
  with some degree of insurance against business cycle risk. These
  contracts make the fraction of labor income in output (or consumption)
  countercyclical, while the fraction of earnings in output is
  procyclical. Gomme and Greenwood (1995) document that these
  business-cycle-related changes in labor income and earnings can be
  found in many countries.
  CORPORATE EARNINGS AND THE EQUITY PREMIUM

Finally,
On the cyclical allocation of risk P.117 shows that Labor’s share of income (in Australia, Austria, Britain, Canada, France, Germany, Japan, and the USA) is negatively contemporaneously correlated with the seasonal component of aggregate income, suggesting that when aggregate income is high profit margins are high.


Has US real median personal/household income been stagnant since the late 1970's/early 1980's?

As said in the comments, there is an important difference between household income and wages. Household income includes income from capital. As a higher share of total income is going to capital than to labour (see below), you would expect a divergence between household and wages.

Similarly, productivity increases are not being translated into higher wages, which means a higher distribution of gains toward capital (see below).

(Source of image here)

Another important issue is that the median can be a misleading figure when distributional changes are occurring. For instance, below is the percentile level of household income for the US:

(Source of image here)

As you can see, the median can mask quite an heterogeneous development for different percentiles. Since lower percentiles source their household income mainly from wages, a stable median wage but an increasing median income is entirely consistent with higher inequality plus lower labour share.


What does 'currency convertibility' mean?

This definition of a freely convertible currency may be clearer to you:

  Freely convertible currencies (also called as fully convertible or permitted currencies) are those that anyone can convert into another foreign currency without restrictions or interventions by government of the original country.

It makes clear that the "ease" you refer to typically refers to the lack of government restrictions.

The stereotypical case of a non-convertible currency is one for which the government maintains a fixed exchange rate.  Imagine the UK pegs £1 to be worth \$2, although the market value (i.e. the equilibrium if restrictions were lifted and the goverment didn't intervene) is only \$1.80, say.  In order to maintain this exchange rate, the UK government has to be willing to buy sterling for a price of \$2 from anybody that wants to sell it.

Often this leads fairly quickly to the UK government running short of dollars, at which point the temptation is to try to bring in some sort of restrictions on who can buy dollars, in what circumstances, how long they have to wait for them to be delivered, etc. -- in order to slow the outflow of foreign exchange.  They may also introduce laws that say that when British firms sell goods abroad, they have to convert the dollars they earn to sterling through the central bank, to provide a bigger source of foreign exchange.  They may also introduce laws banning the conversion of pounds by banks, street traders, etc.

By this point the "ease" with which sterling can be converted is pretty low; it has low convertability.

In the example you mention, the UK suddenly removes restrictions, returns the pound to full convertability, and buys dollars from anybody that wants to sell.  In this situation, you'd expect all sorts of people to rush to buy dollars from the UK government at the favourable rate before they run out and are forced to reintroduce restrictions.


You are missing a $\min$ operator just before the bracket. The utility maximization problem is as follows, $$\max \ \min [\alpha x_1, ..., \gamma x_3] \\ \ \ \text{such that} \ \ \lambda_1x_1 + ... + \lambda_3x_3 = M$$
Consider the case of two goods with utility $u$ given by $u(x) = \min[\alpha x_1, \beta x_2]$. At the optimum, what do you know about the relation between $\alpha x_1$ and $\beta x_2$? They must be equal, that is $$\alpha x_1 = \beta x_2$$ For if not, then assume without loss of generality that $\alpha x_1 > \beta x_1$. What is the utility of such choices of $x_1$ and $x_2$? It must be $\beta x_2$, which means that some of your money is being spent on $x_1$ (assuming prices are strictly positive) but it is not giving you any extra utility, and so this cannot be an optimal choice of consumption.

It follows that the equality must hold at an optimum (this is obvious graphically too). Along with the budget constraint, that gives you two equations and two unknowns, which can be solved for optimal consumption. A similar approach can be applied to the case of $n$ goods.

Of course above is assuming that we are dealing with a trivial utility maximization problem, and aren't doing integer programming and the like.


I think you are making it unnecessarily hard for yourself. You can calculate the deadweight loss (DWL) simply as the difference between consumer surplus pre-tax and post tax, and also by adding tax revenue as it is not necessarily wasted if government uses it, so that would be:
$$DWL =  \frac{(A−ap)^2}{2a} -  \frac{(A−a(p+t))^2}{2a} +t(A-a(p+t))= A t - \frac{1}{2} \left( a t (2 p + t) \right) + t(A-a(p+t)) $$
In addition if you have further equations that explicitly model supply you should also add to the deadweight loss the difference between producer surplus pre-tax and post tax but if that is not specified it is common to assume that supply is just flat line (so there is no producer surplus).


GNI versus GNP for Measuring Development

GNP measures production, so is a better measure of how the country is doing in the long term at accumulating fixed capital and developing. Fixed capital has a much higher return than the financial capital that GNI is typically derived from. However, both measures are usually very similar, except for some tax havens such as Ireland that have significantly more GNI than GNP.

Suppose a country consisted of one individual making trillions of dollars on the stock market and boosting GNI without any national product. If they don't own any fixed capital, this would be a very bizarre economy and not really "developed".

On the other hand if a country has lots of production but has a negative GNI because, say, a domestic company issued a massive dividend to overseas investors that year, then the country could still be called "developed" because it has real people and factories and so forth.

Neither of these cases have anything to do with reality of course, they're just examples.


Envelope theorem for discrete choice sets?

There is an envelope theorem for the setting you describe.

Have a look at “Envelope Theorems for Arbitrary Choice Sets” by Milgrom and Segal (2002).


Jokes in Economics; Challenging Assumptions in the Dismal Science

Not to suggest that this should become a list of the best econ related https://xkcd.com/552/ and http://phdcomics.com/comics.php but I always really liked that one:


Can infrastructure costs be covered by taxes on flat sales/rents?

Yes,

What you are describing is called value capture. There are a variety of forms of the tax, each with their own pros and cons. The general idea is that infrastructure, particularly transport infrastructure, disproportionately benefits parties closest to the line.

A good example is happening in Western Sydney in what was semi-rural land. An international airport and substantial extra development. Large land-owners found that their property is just down the road from a planned town-centre and they can build apartments on their paddocks. There was massive value uplift which was not captured by the government, but could have been.

I'm not aware of a reason why the policy shouldn't work, but it is politically tricky.


Monopoly equilibrium with a completely inelastic demand

Perfectly inelastic demand means quantity demanded is $q$ irrespective of the price. If producing quantity $q$ costs $c$ then the monopolist's problem is

$$\max_p \{pq-c\}.$$

This problem is not well-defined because the function $pq-c$ is increasing in $p$ and has no maximum. So there is no solution to the monopolist's problem. Intuitively, since the monopolist can sell $q$ units at any price, it would want to choose a price of $+\infty$.

You remark incredulously that we do not observe infinite oil prices. If the price of fuel increased to \$1000 per gallon then people would obviously drive their cars less and demand less fuel. So the demand for oil is not perfectly inelastic!

One way to make the problem more well behaved, which is quite common in the literature, is to assume a so-called "choke-price" (a price above which demand drops to zero). Demand is therefore
$$D(p)=\begin{cases}q\text{ if }p\leq\overline{p}\\[0.3em]0\text{ if }p>\overline{p}\end{cases}.$$

The monopolist's problem is then

$$\max_p \{pq-c\}\text{ s. th. }p\leq\overline{p}.$$

This is a well-defined maxmisation problem with a unique solition: $p=\overline{p}$ (assuming $\overline{p}q<c$, otherwise the firm would just shit down).


Provided that increasing returns to scale apply over the whole production function of the company it is likely that it would become natural monopoly. For example, Mankiw in Principles of Economics (pp 292) in some passages even defines monopolies in relation to their cost  function:

When a firm’s average-total-cost curve continually declines, the
firm has what is called a natural monopoly.

Generally the average-total-cost curve will continually decline when firm has increasing returns to scale.
However, there are models with increased returns to scale where monopolies are not inevitable. For example, Wang (2016) presents a model of banking competition where firms face increasing returns to scale, and even though the model predicts increase in market concentration it does not necessary predict that a single bank will become monopoly.


Is there an economic theory that explains what happens when a market is saturated with a precious resource?

At the outset, discovery does not equate availability. Huge amounts of platinum (or the material at issue) might be discovered, but that does not necessarily mean that its extraction is feasible or practicable with the currently available techniques. See here and here.

Assuming that the discovered material is economically available, then supply would increase, thereby pushing prices down. At the same time, demand for that material would increase because agents (consumers) might deem that material a convenient substitute of other [now relatively costlier] alternatives. The net effect would be a higher quantity of that material being traded, and at a lower price.


Example of an economy where Equilibria may not be efficient, where one agent is altruistic

This is an old question. The first example of inefficiency caused by altruism that I know of is due to Winter, S. (1969). A Simple Remark on the Second Optimality Theorem of Welfare Economics. Journal of Economic Theory, 1, 99–103, but I am sure others were around before that.
Now, the example in Winter(1969) does not answer your question because it features altruism for all agents in the economy. As mentioned by The Almighty Bob you can find another example in Heidhues and Riedel (2007). Another good reference is Dufwenberg, M., Heidhues, P., Kirchsteiger, G., Riedel, F., & Sobel, J. (2011). Other-Regarding Preferences in General Equilibrium. The Review of Economic Studies, 78(2), 613–639, which is a summary paper of Heidhues and Riedel (2007) and contributions on the topics by the other authors.
Without more constraints on the preferences, it is not hard to find an example satisfying the conditions of your question. I am sure you could find it yourself, if you haven't already. But in order to make the answer complete here is one:

$\Omega \equiv \{(1,1),(1,1)\}$ (individual endowements)
$U_1 \equiv \min \{x_1,y_1\}$ (perfect complement, no altruism)
$U_2 \equiv \min \{x_2, y_2\} + 2*U_1$ (perfect complement, with (strong) altruism toward $1$)
Walrasian equilibrium = {(1,1),(1,1)}, together with whatever (finite) relative price you like.

I guess it is clear that the Walrasian equilibrium is not Pareto efficient. Notice that this "result" highly depends on the definition of the consumption space. If you add

Donation of good $x$ from $2$ to $1$, and
Donation of good $y$ from $2$ to $1$,

to the consumption space, then you would recover efficiency (although it might take a little care to define a meaningful notion of Walrasian equilibrium in this case).


Concave utility functions corner solution explanation

Intuitively, one can see that the corner solutions lie on the highest possible indifference curve(for concave preferences). Thus, utility obtained will be highest on those points. The following figure(courtesy-Google Images) might help one to visualize the above statement.

Mathematically, it is a bit tough to visualize whats going on. However, the Kuhn Tucker conditions can tell you whether a corner solution will exist or not(note that concavity/convexity of preferences are of no use while using Kuhn-Tucker Theorem, however one needs to check for differentiability of the objective function/constraints). First of all, according to Weierstrass' Theorem, it is guaranteed that an optimum point for $U(x_1,x_2)$ exists in the budget set $B(p_1,p_2,w)=\{(x_1,x_2):p_1 x_1 + p_2 x_2 \leq w\}$ (only if $U(x_1,x_2)$ is continuous in the given domain,i.e; the budget set). This tells us that we cannot rule out any solution/optimal point where either $x_1=0$ or $x_2=0$, or both. Now, to check whether a corner solution exists or not, we can proceed as follows. Consider the maximization problem
\begin{gather*}
\max\ U(x_1,x_2); \\ s.t.\ p_1x_1+p_2x_2\leq w;\ x_1\geq 0, x_2\geq 0.\\
\end{gather*}
The Kuhn Tucker Lagrangian is given as
\begin{align}
L = U(x_1,x_2)+ \lambda(w-p_1x_1+p_2x_2)+ \mu_1 x_1+ \mu_2 x_2.
\end{align}
The Kuhn Tucker conditions are given as-
\begin{gather*}
\frac{\partial L}{\partial x_1}\leq0\tag{1}\\ \frac{\partial L}{\partial x_2}\leq0\tag{2}\\ \frac{\partial L}{\partial \lambda}\leq0 ,\ \frac{\partial L}{\partial \mu_1}\leq0\ ,\frac{\partial L}{\partial \mu_2}\leq0\tag{3}\\ \lambda\frac{\partial L}{\partial \lambda}=0\tag{4}\\ \mu_1\frac{\partial L}{\partial \mu_1}=0\ ,\mu_2\frac{\partial L}{\partial \mu_2}=0\tag{5}
\end{gather*}
The inequality signs in conditions $(1)$ and $(2)$ can be replaced with the equality sign, if the constraints are binding(you can read more about binding constraints/constraint qualifications from Simon and Blume).
However, the given problem of corner solutions can be understood from the conditions in $(5)$, which say that $\mu_1 x_1=0$ and $\mu_2 x_2=0$. These equations tell us that either any one(or both) of the $\mu_i=0$ or any one(or both) of the $x_i = 0$. By considering all the above cases one by one, we can figure out whether a corner solution will exist or not.
For example, if the utility function is given as $U(x_1,x_2) = x_1 + \ln(x_2)$, the solutions obtained by using Kuhn Tucker theorem are-
\begin{gather*}
    x_1=
\begin{cases}
    \frac{w}{p_1} - 1,& \text{if } w>p_1\\
    0,              & \text{otherwise}
\end{cases}\\
x_2=
\begin{cases}
    \frac{p_1}{p_2} ,& \text{if } w>p_1\\
    \frac{w}{p_2},              & \text{otherwise}
\end{cases}
\end{gather*}
The corner solutions in this case are obtained whenever $w\leq p_1$.


An obvious way to approach this would be to compare projections based on a China without the one child policy (pre 1979 pop growth rates) and on a China with the one child policy (post 1979 pop growth rates). Pop growth rates are constant but different in the two cases.


Unfortunately, I'm not aware of any publicly-available concordance, and I can promise you that there's no clean mapping between UCC and NAICS. Obviously, some categories will map well at low granularity (UCC 190903 will definitely map to 722, for example).

While I'm sure it's fine for whatever you're doing if multiple UCC codes map to a single NAICS code, the big problem is that some UCC codes will map to multiple NAICS codes. For example, 190113, "lunch at vending machines and mobile vendors" will map to some combination of NAICS 454210, "vending machine operators" and NAICS 722330, "mobile food services".

The way this is usually handled is through some form of allocation— you assume, for example, that the CES population you're observing has exactly the same distribution of expenditures as the overall economy.


Can the money multiplier happen within a single bank / a single loan?

From what I saw, the video confused various forms of monetary instruments.
If a customer gets a \$1000 transfer from the Federal Reserve, Bank A has a \$1000 deposit liability, and a new \$1000 balance at the Federal Reserve.
Under the assumption that the bank was at its reserve limit, it has \$900 in excess reserves.
It can eliminate those excess reserves by making a \$9000 loan, as that creates a deposit with \$900 of required reserves. The growth chain ends there.
In practice, the proceeds of a loan are typically spent quickly. As such, the bank would expect a need for an outflow of the size of the loan. It needs to hold liquid assets that can be liquidated to meet the outflow, or have the capacity to borrow in the inter-bank market.
Given the unpredictability of cash flows, in the pre-2008 era in the US (one of the few places with formal reserve requirements), banks made loans, and then borrowed in the Fed Funds markets to make up deficiencies. The Fed supplied the Fed Funds market the amount required to hit its policy interest rate target. (Currently, reserve requirements have been abolished, and there is a permanent excess of reserves.)
The presentater probably relied on out of date textbooks.  This paper by BoE staff explains how the “money multiplier” is a misconception: link to working paper.


SIR Model and Information/ Innovation Economics

I'm not familiar with this area, so I can't tell you what are the seminal works, etc., but a quick search finds Gurley & Johnson (2017) "Viral economics: an epidemiological model of knowledge diffusion in economics":

  We model the diffusion of economic knowledge using an epidemiological model of susceptible, exposed, infected/inspired, and recovered populations (SEIR). Treating peer-reviewed journal publications as evidence of a scholar’s infection/inspiration with energy for an idea or theme, we estimate the coefficients of a four-equation simultaneous system for each of 759 sub-fields of economics. Results show that some sub-fields of economics (or viruses, here) grow endogenously much faster than others, and just over half have basic reproduction properties sufficient to ensure survival without the annual addition of new protégé scholars.

Which in some sense is amusing because it's applying the model to economics knowledge/research itself. (The paper looks like it has been in draft out there for a while; there's one dating back to 2011, which is freely available on SSRN.)

I suspect the paper cites some other relevant work(s) in this area.


Why can't countries print another country's currency?

Why assuming that it isn't done in the first place? Turns out, it was actually done by Nazis in WWII

https://en.wikipedia.org/wiki/Operation_Bernhard

Also, suppose that somebody actually made dollars that are indistinguishable from genuine, Fed-printed dollars. How would you find out in the first place? How sure are you that nobody is doing it? :)


In my view, it is useful to distinguish two phenomena:

individuals have time-inconsistent preferences (this fits your example).  There is a huge literature on that. The common practice is to evaluate welfare from the ex ante perspective, thereby assuming that people have well-defined preferences over the long-run, but that their choices might deviate from it because of immediate impulses. In that case, the true preference can be elicited by asking the individual to choose in advance. Some authors question the ex ante perspective, see for instance "The Welfare Economics of Default Options in 401(k) Plans" by Bernheim, Fradkin & Popov.
individuals make mistakes because of cognitive limitations. In this situation, it is very hard (impossible?) to recover the true preferences from the choices. At best, we can have a measure of the extent to which the individual makes consistent decisions. You can have a look for instance at "The Money Pump as a Measure of Revealed Preference Violations" by Echenique, Lee & Shum.

Overall, it is true that economists usually reject stated preferences, and there are many good reasons for that. It does not necessarily imply that we assume that observed choices are perfect; just that we do not have a better alternative to elicit people's preferences. However, some researchers are now studying how we could take advantage of non-choice data: (i) from a neuroscience perspective (many recent papers by Antonio Rangel and Colin Camerer for instance); (ii) using stated preferences, like "Non-Choice Evaluations Predict Behavioral Responses to Changes in Economic Conditions" by Bernheim, Bjorkegren, Naecker & Rangel.


Karush-Kuhn-Tucker in infinite dimension

Yes, Bachir et al. (2021) extend the Karush-Kuhn-Tucker theorem under mild hypotheses, for an infinite number of variables (their Corollary 4.1).
I give hereafter a weaker version of the generalization of Karush-Kuh-Tucker in infinite dimension:

Let $X\subset\mathbb{R}^{\mathbb{N}}$ be a nonempty convex subset of
$\mathbb{R}^{\mathbb{N}}$ and let $x^{*}\in Int\left(X\right)$. Let
$f,g_{1},g_{2},...,g_{m}:X\rightarrow\mathbb{R}$ be convex functions
continuous at $x^{*}$ and term-to-term differentiable at $x^{*}$, i.e.
such that the functions
$f_{n,x^{*}}\left(x_{n}\right):=f\left((x_1^{*},...,x_{n-1}^*,x_n,x_{n+1}^*,...)\right)$ and
$g_{j,n,x^{*}}\left(x_{n}\right):=g_{j}\left((x_1^{*},...,x_{n-1}^*,x_n,x_{n+1}^*,...)\right)$ are
differentiable at $x_{n}$ for all $n\in\mathbb{N}$ and
$j\in\left\{ 1,2,...,m\right\}$.
(Qualification condition) Suppose that for all $k\in\mathbb{N}^{*}$
and for all $x\in X$,
$$x^{*}+P^{k}\left(x-x^{*}\right)=\left(x_{1},...,x_{k},x_{k+1}^{*},x_{k+2}^{*},...\right)\in X$$
If there exist
$\left(\lambda_{j}^{*}\right)_{j}\in\left(\mathbb{R}_{+}\right)^{\mathbb{N}}$
such that
$$\lambda_{j}^{*}g_{j}\left(x^{*}\right)  =0,\:\forall j\in\left\{
1,2,...,m\right\} \quad \quad \quad \quad \quad (1)$$
$$f_{n,x^{*}}^{\prime}\left(x_{n}^{*}\right)+\sum_{j=1}^{m}   \lambda_{j}^{*}g_{j,n,x^{*}}^{\prime}\left(x_{n}^{*}\right)=0,\:\forall
n\in\mathbb{N} \quad \quad (2)$$
(Sufficiency) Then $x^{*}$ is an optimal solution on $\Gamma:=\left\{
\left(x_{i}\right)_{i}\in
X\,:\,g_{1}\left(x\right)\leq0,...,g_{m}\left(x\right)\leq0\right\} :$
$$f\left(x^{*}\right)=\underset{x\in\Gamma}{\inf}f\left(x\right)$$
(Necessity) Besides, if $x^{*}$ is an optimal solution on $\Gamma$ and
if the Slater condition $Int\left(\Gamma\right)\neq\emptyset$ is
verified, then there exist unique
$\left(\lambda_{j}^{*}\right)_{j}\in\left(\mathbb{R}_{+}\right)^{\mathbb{N}}$
which verify the (Karush-Kuhn-Tucker) conditions (1) and (2).

The number of constraints has to be finite, but simple constraints like non-negativity constraints can be replaced by an equivalent restriction on the domain of the variables. For example, instead of the constraints $\forall n \in \mathbb{N},\;x_n \geq 0$ on the domain $\mathbb{R}^{\mathbb{N}}$, one can take $X=(\mathbb{R}_+)^{\mathbb{N}}$, and the theorem applies.
Note that the (sufficiency) result is easy to prove when one further assumes that the convex Lagrangian $\mathcal L(x, \lambda)=f(x)+\sum_{j=1}^m\lambda_j g_j(x)$ is Gateaux differentiable, with a Gateaux derivative equal to 0 at $u=(x^*, \lambda^*)$.
Indeed, a function $h: V \rightarrow \mathbb{R} $ convex and Gateaux differentiable on $V$ verifies $h(v)-h(u) \geq h^\prime(u; v-u), \forall u,v \in V$, where $h^\prime(u; v)$ is the directional derivative of $h$ at $u$ in the direction $v$. (One can see that from the definition of convexity: $h(u)+\theta \left( h(v) -h(u) \right) \geq h\left(u+\theta (v-u)\right)$; subtracting $h(u)$, dividing by $\theta$, and taking the limit when $\theta \rightarrow 0^+$; see this for more details). Applying that inequality to the Lagrangian at $u$ proves that the Lagrangian admits a minimum at $u$, which solves the minimization program: $ f(x^*) =L(x^*, \lambda^*) \leq f(x)+\sum_{j=1}^m\lambda_j g_j(x) \leq f(x), \; \forall x\in \Gamma$.
However, in general, it is not easy to prove that the Gateaux derivative of a convex series (such as an infinite Lagragian) (exists and) equals 0 at some point $u$, unless one uses the result (Theorem 3.14 in Bachir et al. (2021)) that the Gateaux derivative is thus equal to the sum of derivatives of each term in the series.


Let matrix $A = \begin{bmatrix} p_1 & p_2 & \ldots & p_n \end{bmatrix}$. Let $\mathbf{x}^*$ be a fixed solution to $A \mathbf{x} = c$. Then for any vector $\mathbf{u}$ that belongs to the null space of $A$, we have $A \mathbf{u} = 0$ hence $\mathbf{x} = \mathbf{x}^* + \mathbf{u}$ is also a solution (furthermore, all solutions $\mathbf{x}$ can be written this way). The dimension of the set of solutions to $A\mathbf{x} = c$ is thus the dimension of the null space of $A$.

By the rank-nullity theorem, the rank of matrix $A$ plus the nullity (dimensionality of the null space) of matrix $A$ equals the number of columns of $A$:

$$ \operatorname{rank}(A) + \operatorname{nullity}(A) = n$$

Matrix A is rank 1 (assuming not all $p_i$ are zero) and hence the nullity is $n-1$.


Profit maximization, when $MC = P$, in a simple supply table and curve

What your school economics textbook must say is that :

"In a Perfectly Competitive Market (PC), firms maximize profits when MC= P ". This equality applies only to firms in a perfectly competitive market.

This is because, in a Perfectly Competitive Market there is an infinite number of buyers and sellers and the prices are set by the overall market forces, and the firms and the consumers take that price to be given.

Using Calculus, it can easily be shown that MC=P is the condition for Profit Maximization in PC markets.

We can further see other conditions of maxima using second-order conditions (which relate the slope of the MC curve in relation to the slope of MR curve ) but we can ignore those for now.

Intuitively, a producer earns P on each unit sold. If the MC < P, they earn more on each marginal unit than they spend on its production, and hence they would keep producing more. If MC > P, then the cost of producing each marginal unit is more than the revenue it fetches and hence they would cut down on production. Neither of these is a position of equilibrium for the firm. Hence MC=P is the position of equilibrium and the First Order Condition for profit maximization.

Yes
Yes
Yes, you can't answer this question, because the data set given to you is made up of discrete points.


Where/What should I search to find the big companies annual paid salary information?

I believe there is no comprehensive source of the information collected in a standardized way that you are looking for.

If you want to have info on a single company only, I guess your best bet is to look at the companies' annual reports. Companies disclose a lot of information about their operations there.

Some newspapers certainly did investigations on the topic and collected (spuriously, I guess) some info on that. Try to check the Wall Street Journal, the Financial Times and the Economist for the starter.

If you look for the aggregated statistics by countries, try to search the web-site www.statista.com and the works done by scientists like Gabaix and Branco Milanovic. They both did much of empirical work in inequality and could gather much info on that. No guarantee they published their datasets free of charge.


Why does a reduction in money demand lead to a rightward shift in the LM curve?

If the demand for money falls as the supply remains constant, there is excess supply of money. It is equivalent to an increase in the supply while the demand stays constant.


Counter example for strong and weak Pareto optimality

There are two goods and two consumers, no production. The aggregate endowment is strictly positive. Consumer 1 cares only about the amount of good 1 they consume, with more being better. Similarly for consumer 2 and good 2. Preferences here are continuous and monotone but not strictly monotone. The allocation in which consumer 1 consumes the entire aggregate endowment is weakly Pareto optimal but not strongly so.


What measure of unemployment is used by central banks to find out whether the rate of unemployment is below the natural rate of unemployment?

The phrase “natural rate of unemployment” has been largely replaced by NAIRU or similar concepts. Each country has its own definition of the unemployment rate, the U.S. “headline” measure is U-3.
Nobody has created an estimate for NAIRU that is based on a function of other variables, instead it is estimated based on the behaviour of the economy and the chosen unemployment rate measure. This implies that a NAIRU estimate is defined relative to the unemployment rate used in the estimation procedure. In the United States, most researchers would run their estimation versus U-3, but one might need to validate this.


Is there any variable commonly used as a proxy for the inflation target set by the Central Bank?

Actually Costa Rica publishes inflation target. See the graph below provided by IMF where the inflation target is given by orange band (it is completely normal to target range rather than precise number):

There are no proxies that can be just lifted from statistics that I know of but there are ways how to discover how central banks set monetary policy even if it is not public.

For example, you can start with specifying some possible rules that central bank could be following (e.g. Taylor rule etc.) and afterwards you can conduct an econometric analysis that can show you how much the central bank behavior matches the 'guessed' monetary policy rule. Or alternatively you can deduce it from case studies of central bank's decisions. There are actually many ways how to do this and it is beyond the scope of stack exchange to provide extensive overview of them all.
Commonly used variables to do this are the interest rates that central bank sets, wider interest rates, inflation itself, inflation expectations, money supply aggregates and many more. Have a look at the literature below. However, there is no simple proxy as far as I know.
Examples of such empirical papers include Castroa, (2011); Bec, Salem, & Collard, (2002); Bernanke, and  Mishkin (1992); McGouldrick, (1984). Of course, the list is not exhaustive I tried to include diverse range of papers on this topic. You should have look at the sources cited therein and do additional search based on the keywords you find.


If it is about European Union, it would make sense to look at austerity as a response to the debt crisis. I would measure austerity not as the level of government debt, but as the change in levels of government debt from year to year. The slower the government debt (as a share of GDP) increases, the more austere are the budget policies of that country.

Also, if you have access to state budget deficits data, it would be a very convincing measure of austerity in my opinion. The less is the budget deficit, the more austere is the budget policy.


I do not know Norway's economy. BTW, based on what you report, I would first rearrange your points into a more chrono-econo-logical order. Briefly,

3 - Facing the loss of income due to the massive drop in the crude oil price, --
depending on the impacts that this drop may have in term of income redistribution -- Norway's economic agents may have increased their saving rates $\iff$ economic agents consume less. Less demands means weaker upward pressure on prices, which in turn means less inflation.

1 - Facing the increasing masses of savings, Norway's central bank may have started to decrease the interest rate so as to disincentive agents to do so. Indeed, lower interest rates $\iff$ lower rates of return on savings -- if commercial banks propagate this rate change, which is very likely.

2 - Finally, since these lower interest rates tend to attract less foreign investments -- and since, to access to Norway's lending market, those must first buy home country's currency -- this exerts a downward pressure on the value of Norway's currency, which in turn causes a relative price increase of imported goods $\iff$ imported inflation.

To conclude, I would say that your points 1 and 2 are as correct as they are incomplete.


Zero Economic Profit and opportunity cost?

Theoretically it is possible but only if the non profit organization operates at a cost which is lower than all other existing as well as expected new players as in a free market anybody with a much lower cost can enter and offer a price lower than the NPO and still earns a profit. And what about other NPOs in the same field. If they have a lower cost they will certainly offer a lower price. Secondly the NPO should accepted by the consumers. Practically speaking the market has many more complexities such as existence of brands and the information that price suggests. A lower price means lower quality which may not be accepted by the consumers. For example I may start manufacturing and selling mobile handsets at zero profit but still I might not drive Apple out of the market. Also, the world is not much perfect. To start I would have to install a capacity that can produce at a cost lower than the existing players and then offer a lower price to drive them out.

But still you can have examples of apparent monopolies in markets say for example 'online information'. What do you think Wikimedia is ? It is an NPO.


What are exactly considered resources in modern economics?

What I think you want to consider to be grouped with land, labour and capital is another factor to be considered with labor- technology (A.K.A "Knowledge*).

The term "technology" here means an augmenting factor which is external to the present amount of labour. This can mean improved machinery to produce goods and services or alternatively, this can be viewed as a improvement in human capital which is what training employees can be viewed as in terms of technology.

For more information about the effect of technology/improved human capital in economics, I would recommend checking out the Solow Model and the Skill premium Model

Hope this is helpful!


What did "18/9", "25/", and "30/" mean in this 1800 British document?

Yes your guess is correct.

In fact, the forward slash punctuation mark "/" actually comes to us through its being the abbreviation for the English shilling.

From Humez and Humez (2008):

  ... forward slash (/), which is also variously known as a solidus, virgule, or just plain slash, when the deletion of a single character is to be marked. The solidus was a Roman coin and is ultimately the basis for English soldier, the idea being that a soldier is someone who fights for money, or, as we might say today, a mercenary (from the Latin merces ‘wages.’) The solidus as slash is historically a straightened-out S, the abbreviation for the English shilling (as in 2/3—two shillings thruppence), the step from one coin to another being relatively easy. How we get from shilling to slash in its various other uses—as proofreader’s strike-out mark, arithmetical sign of division, general separatrix, and so on—is rather more murky.


Why does slowly decaying ACF indicate that a time series is non-stationary?

The (simplified) definition of a stationary process is that the mean and variance of the process are constant over time.

If the ACF is slowly decaying, that means future values of the series are correlated / heavily affected by past values. If past values of the series are high, the future values should also be high. If the value goes up, future values will also go up (assuming positive auto-correlation here). This means the mean will change over time, which means that the process is non-stationary.


What are the textbook-like obvious advantages and disadvantages of tipping?

Interesting question. Michael Lynn, prof at Cornell, works on Consumer Behavior and Tipping. According to him, some restaurateurs may rely on tips to

motivate servers to deliver good service,
measure server performance, and
identify dissatisfied customers.

All of those uses of tips assume that service quality has a large effect on the size of tips that consumers leave. He examines and challenges that assumption in his meta-analysis on Restaurant Tipping and Service Quality. He finds a tenuous relationship that can be depicted in the following figure, which is based on one surveyed paper.

Then, the question is: what does explain the change from tipping to no-tipping service? My answer is unfortunately more opinion-based than fact-based. It could be that

managers are aware of this low correlation and want to
experiment a different management based on a fixed payment instead
of a variable one;
due to the financial crisis customers tip less than usual and so managers have to fix the tip to offer better (fixed) wages and attract good waiters.

Update: Note that the no-tipping strategy may have some drawbacks:

Attrition. Servers may change to tipping places in order to get higher wages
Higher prices may lead to lower demand by some customers.

More on the following nice NPR podcasts on

Why Restaurants Are Ditching The Switch To No Tipping. The idea of the no-tipping movement has been to rectify a basic pay unfairness to even out the pay between tipped and untipped employees. Why are they switching back to tipping? Attrition. They were losing staff, servers mostly.
Why Do We Tip?. You can listen to Michael Lynn on this podcast.
The No-Tipping Point on Freakeconomics Radio. Interesting questions on How much would the restaurant have to raise prices in order to pay its waitstaff what they were losing in tips? How much of a raise would kitchen workers get? Would servers now earn a lot less? And if so, wouldn't they all just quit? Would customers get the service they were used to if they didn’t retain the leverage of the tip?


Do points inside the PPF curve always mean productive inefficiency?

Well the problem here is in a set up. It is inconsistent to say that firm is producing below PPF not due to underutilization of factors of production but due to cash, since cash will not enter production function, or if you assume that cash is here used to buy factors of production it is inconsistent to say that firm fully utilizes all avaiable factors while having excess cash.
For example, take very simple production function $F=L^{1/2}$ and firm has access to 100 units of labor, with all the standard background assumptions. I used this simplistic case because here PPF is just one point instead of set of points, and in this case the PPF would just be point F=10. If firm would be using 100 units of labor it would be operating at PPF and if not it would be operating below PPF. However, you cant just arbitrary announce that this firm  is producing F=8, due to not using all cash if cash does not enter as factor of production. That is simply inconsistent with having production function $F=L^{1/2}$
Furthermore, let us assume that firm does not just have 100 units of labor but that it has 100 dollars and has to purchase labor on labor market for 1 dollar per labor. Well in this case if firm has still some excess cash, for example if it purchased only 70 units of labor, then it still does not uses all factors of production in most efficient manner because given its 100 dollars it still has 30 units of labor avaiable for hire that it could use. Consequently, the problem in the setting above is that it just arbitrary announces that firm is utilizing all factors of production as much as possible while not being at PPF. That is like asking, from math we know that unit circle is given by all points that satisfy equation $1=x^2+y^2$, but I assume point (100,10) lies on unit circle and it clearly does not satisfy the equation. It is just inconsistent reasoning.


Well this all boils down to the economic reason why cities exists at all.

The economic reasons for cities to exist are:

Division of labor - as was famously observed by Adam Smith “It is the great multiplication of the productions of all the different arts, in consequence of the division of labour, which occasions, in a well-governed society, that universal opulence which extends itself to the lowest ranks of the people.”

Virtually all economists agree that division of labor leads to higher productivity and hence incomes and the higher population of certain area is the more possibilities for specialization there are. In a city with 5000 people there will be less scope for specialization than in city with 5000000 people. This is so because for example in a small community there might not be enough demand to sustain some very specialized goods or services. For example, I play a lot of board games when I was living in a small city there were just two board games shops with just general selection of games. Now I live in big city and there are separate shops for table top games, separate shop for just warhammer figurines, another for mainstream board games, separate shops for indie board games etc.

Increasing returns to scale -  most firms will have them at least at a portion of their cost curve. More people - means that firms there can scale up their production and increase their profit. This in turn leads to higher demand for workers and also to higher wages. There will be also some macroeconomic effects no matter for which factor of production the returns increase.

transportation cost - Last factor connected to the population is transportation costs. If everything is produced and exchanged in one hub people and businesses incur lower transportation costs than if everything is produced in separate locations.

So to sum it up as population density of certain are increases incomes should increase as well. There is also a downside that as population density increases price levels increase as well due to higher overall demand, but as long as people are free to move they would only move to the city if the increases in income outpace price level increases so empirically you will observe that as population of urban area increases the real incomes will rise as well.


Equilibrium value of $b$ (market tightness) in a search model

From

$$ 1 - e^{-b} = a_{F}(b) = \frac{k}{y (1-\beta)}$$

we get

$$b = -\ln\left(1-\frac{k}{y (1-\beta)}\right) \tag{1}$$

For this to exist (be a real number) it must be the case that

$$1-\frac{k}{y (1-\beta)} > 0 \implies \frac k y < (1-\beta) \tag{2}$$

which is already assumed.

Then, uniqueness of the solution of $(1)$ is guaranteed because all arguments/parameters in the right-hand side enter only once. So for any vector $(y,k,\beta)$ satisfying what it needs to satisfy, we get a unique $b$.


How do we explain the difference in average leverage between banks and non-financial firms?

The point @EnergyNumbers raises is correct, and it's easy to understand from an intuitive standpoint: One of the key roles of financial intermediaries is to match the demand for liabilities of a given tenor to the demand for assets of a given tenor.

Financial intermediation allows maturity mismatches to exist in non-finance sectors of the economy by taking the opposite position. To do this, though, financial intermediaries such as banks must have far higher leverage than firms in the rest of the economy.

Consider, for a moment, the tenors of assets that consumers hold: outside of retirement investments (for consumers who are far from retirement age), most consumers have long-dated liabilities (mortgage loans, student loans, etc.), and short-dated assets (bank deposits, money market fund holdings, etc.). This mismatch exists for good reasons. On the liability side, for example, it's not insane for someone to want to own a house and consume housing services before having 100% of the cash necessary to purchase one outright. This sort of consumption smoothing, where one with a long horizon for future earnings brings forward a fraction of their lifetime consumption, is generally considered to be optimal behavior from a utility standpoint.

At the same time, on the asset side, consumers have to be able to smooth shocks to their income, and thus having short-dated, liquid assets, like bank deposits, is also optimal. If you're a consumer who holds all his extra cash in equities, you're going to be really sad when the stock market tanks at the same time that you lose your job and need to cash out of the stock market to cover your expenses for the next few months.

Similarly, non-financial firms have the same sort of demand for assets of particular tenors— they need a certain amount of cash for liquidity management, but at the same time, they want to be able to invest in capital projects (purchase equipment, etc.) that will pay off over a long time horizon.

Of course, in a closed system, the tenor of assets has to equal the tenor of liabilities. So how do we balance this? Financial firms take the other side of these trades, accepting significant maturity mismatches.

Non-financial firms are able to do engage in productive investments by making a deal with financial firms in which the non-financial firms give up a share of their future production in the form of interest payments in exchange for long-dated liabilities. Similarly, consumers are able to engage in consumption smoothing by giving up a share of their future labor income to financial firms in exchange for long-dated liabilities. The financial firms, for their part, end up holding a lot of long-dated assets, while being funded to a great extent by short-dated liabilities (such as bank deposits and money market fund holdings through the wholesale funding market). Financial firms make their money on the spread between maturities (the "carry")— the whole "borrow at 3%, lend at 6%, be at the tee by 3pm" thing.

This has a couple of implications:

The amount of liabilities financial firms must hold relative to assets is a function both of the demand for assets and liabilities of differing tenors in the rest of the economy (i.e., the maturity mismatch in the rest of the economy) and of the size of the financial intermediation sector relative to the rest of the economy. The smaller financial intermediation is relative to the rest of the economy, the higher the leverage necessary in financial firms to rebalance maturity mismatches elsewhere.
The existence of significant maturity mismatches in finance means that liquidity risk is concentrated in financial firms, which could lead to bank runs. This is why deposit insurance is used, and also why banks are given access to the Fed's discount window.


Comparing cost of living of one state with that of other states

Quality of life matters too. This sometimes appears in the labor economics literature as "compensating differential". So it's probably not enough to weight cost of living by median or average wage unless you can objectively determine that all other factors are identical (e.g, tax structure, quality of transportation networks, population density, greenspace, etc.).

These factors are in general not likely to be pecuniary, so they are usually treated as a residual - if MW/COL in state A is less than MW/COL in state B, then the difference must be due to improved amenities in state A.  This is obviously unsatisfactory, and as a result there's a lot of active literature in this area.  And they will, of course, be highly subjective.


Developmental studies and industrial policy for the highly developed countries - research trends?

There is a lot of newish research on the adverse consequences of Globalisation and more generally De-industrialization on low skilled workers in developed economies. A good place to start is Autor, Dorn, Hanson in the AER 2013. David Autor has other interesting work on this topic as well as on automation.

Your questions have quite a normative ring to themselves, and it seems to me you already know what kind of answers you want. It is true that discussing the adverse effects of Globalisation or technological progress  on some people in rich countries has become a more active research topic. But I think most people are very still far from questioning free trade or technological progress as Pareto efficient. And I think the overwhelming majority of economists still thinks that industrial policy in a developed country is a silly idea. So I don't think you are going to find this big trend you are looking for.

Also, as a sidenote, even development economics has moved quite far from the kind of research that debated the industrial policy you have in mind. It's mostly about RCTs and institutions now.


Why is the Australian dollar, often viewed by traders as a proxy for the Chinese economy?

I couldn't read the FT article... but...

According to this article here, China imports a lot of raw material from Australia. This means that if Chinese economy is doing well, they import a lot of stuff from Australia... hence AUD will appreciate.

This news piece, for example, says that as China is hit with new tariffs, AUD falls. This is because the Chinese won't be able to export as much, which means they won't demand as much raw material from Australia anymore.

Or this more recent article says that Chinese CPI comes out higher than expected. This means that it's more likely that the PBOC (China's central bank) will need to do something to reign in inflation, slowing down the Chinese economy and Australian exports. This depreciates AUD.


Could a Cap-and-Trade and a Carbon Tax both work together as a system to reduce carbon emissions?

The combination of the two policies could work and would have some advantages over either alone, and for a given reduction in emissions the overall cost burden on industry and consumers would not be doubled.  However, the cap on emissions would weaken the effect of gradual escalation of the tax rate.

The model represented in the following diagram is a simplification in several respects (which will be considered below) but is a helpful starting-point.  It assumes a single homogeneous fossil fuel, with emissions depending solely on the quantity of fossil fuel consumed.  Hence the horizontal axis represents quantity of fossil fuel and quantity of emissions.

Supply is shown as highly elastic (nearly horizontal) because the UK buys large amounts of its fossil fuels, especially coal, on competitive international markets.  In the absence of policy intervention, equilibrium is at point A with consumption and production (assuming no change in stocks) at $Q_A$, which also represents emissions.  Note however that while consumption and emissions relate to the UK, production includes amounts produced elsewhere and imported to the UK.

The effect of the carbon tax is to raise the supply curve vertically by the amount of the tax. In the absence of other measures, the new equilibrium is at B.  Production, consumption and emissions fall to $Q_B$.  The cost burden of the tax is mainly shared between users of fossil fuels and consumers of goods produced using fossil fuels, the shares depending on elasticities in particular markets. A share of the burden would also take the form of a fall in the price received by fossil fuel producers, but this would be small given the elastic supply curve.  The tax revenue could be used by the government in various ways with various distributional effects.

The effect of adding cap-and-trade, assuming an effectively enforced cap at $Q_C$ below $Q_B$, is to limit consumption, production and emissions to $Q_C$.  Thus the part of the demand curve to the right of $Q_C$ becomes redundant.  Equilibrium is brought about via the market for emissions permits, which will determine a permit price such that supply (post-tax) and constrained demand are in equilibrium at C.  Distributional effects depend heavily on the mechanism for the initial allocation of permits.   The ‘corporate welfare’ point in BKay’s answer relates most clearly to the case of free allocation.  This can be avoided if allocation is by auction, with the revenue accruing to the government with, again, distributional consequences depending on how it is used.

Within this model we can infer the following:

The tax and the cap both reduce production of fossil fuel and emissions.  It is not a case of separate policies to achieve separate objectives.
The cap provides certainty that emissions will not exceed a pre-determined level (whereas a carbon tax alone cannot give this certainty because its effect depends on the position of the demand curve which cannot be known with certainty).  The tax provides certainty that there will be some reduction in emissions, as compared with their level in the absence of policy (whereas cap-and-trade alone cannot give certainty in this respect because of the risk that poor forecasting or industry lobbying will result in the cap being set at too high a level, ie above $Q_A$).  In this respect the combination of policies overcomes limitations of either policy alone.
If a firm has to buy its permits at the market price, as well as bear a share of the burden of the tax, it might be perceived that it is subject to double taxation.  However, the effects of the tax and the permit price are additive, so that the total cost to the firm, for a given reduction in emissions, is no more than the cost under either policy alone.
With a fixed cap in place, gradual escalation of the tax rate will not normally produce further reductions in production or emissions.  It will move $Q_B$ gradually towards $Q_C$, increasing the share of emissions reduction due to the tax and reducing the share due to cap-and-trade, but will not shift $Q_C$.  Only if the tax rate were so high, or the cap so lenient, that $Q_B$ were below $Q_C$ (in which case the cap would have no effect) would escalation reduce production and emissions.  To achieve further progressive reductions in emissions within this policy framework, it would be essential gradually to reduce the cap.

Let’s now consider some ways in which the above conclusions need to be modified because of departures from the model’s assumptions.  Firstly, cap-and trade, because it depends on measurement of actual emissions, is for practical reasons normally limited to large users or specific industrial sectors, certainly excluding household use. By contrast, a carbon tax levied at point of entry on the carbon content of the fuel is a simple and effective way of bringing all consumption of fossil fuels within the scope of policy.   In practice, therefore, users within the scope of cap-and-trade would be at (their individual equivalent of) $Q_C$, while those outside its scope would be at $Q_B$.  This difference in coverage could result in emissions reduction not being achieved in the most efficient way, eg by creating a greater incentive for expensive measures by large firms than for simple measures in the home.  In this respect the combined policy would be inferior to a carbon tax alone, but superior to cap-and-trade alone.  A further consequence is that escalation of the tax will always reduce emissions by users outside the scope of cap-and-trade.

Secondly, fossil fuels are not homogeneous.  For example, emissions can be reduced by switching from coal to natural gas in electricity generation.  The combined policy (like either policy alone) would not only reduce production of fossils fuels but also encourage this type of switching.

Thirdly, the assumption that emissions depend solely on consumption of fossil fuel, though currently fairly accurate, may become less so in future.  Currently the main ways in which carbon emissions can be reduced are improved fuel-efficiency, switching to alternative energy sources, and energy-saving measures such as improved insulation.  However, the implementation of carbon capture and storage (see here) may be beginning to weaken the link between consumption and emissions. It is desirable therefore that policy should include an element based on actual measured emissions.  In this respect either the combined policy or cap-and-trade alone are superior to a carbon tax based solely on carbon content.