Difference between Giffen and inferior goods. Why aren't all inferior goods Giffen goods?

Def 1 is wrong. An inferior good is a good for which the demand decreases after a decrease in the agent's income. (not a decrease in the price of the good).

Definition 2 is trying to define the same concept, "an inferior good" so it is also wrong. Instead, both definitions would be appropriate if they were describing a Giffen good.

Giffen goods are indeed a special case of an inferior good. These are goods for which the law of demand does not apply. Since their demand has a positive relationship with price. The reason why this positive relationship is possible is that when prices change, they create income and substitution effect, and when goods are inferior, these effects go in the opposite direction. Thus, if the income effect dominates the substitution effect, you get the (seemingly counter-intuitive) behavior that demand increases when prices go up, and decreases when prices go down (a Giffen good). Note that to be a Giffen good you need to be inferior, but the reverse is not necessarily true, for example, if the substitution effect dominates the income effect.


Economic consequences of a lingua franca

These two papers seem relevant. The large Indian wage premium for English language fluency suggests that there would be significant returns at the margin to providing services through trade instead of locally, but they are limited by the supply of foreigners with the right language skills.  However, these premiums are largest among the skilled workers, suggesting that the benefits of a universal language would also accrue disproportionately (in absolute wage premia) to the most skilled.

  Recent studies have shown that trade liberalization increases skilled
  wage premiums in developing countries. This result suggests
  globalization may benefit elite skilled workers relatively more than
  poor unskilled workers, increasing inequality. This effect may be
  mitigated, however, if human capital investment responds to new global
  opportunities. A key question is whether a country with a more
  elastic human capital supply is better positioned to benefit from
  globalization. I study how the impact of globalization varies across
  Indian districts with different costs of skill acquisition. I focus on
  the cost of learning English, a relevant qualification for high-skilled
  export jobs. Linguistic diversity in India compels individuals to
  learn either English or Hindi as a lingua franca. Some districts have
  lower relative costs of learning English due to linguistic
  predispositions and psychic costs associated with past nationalistic
  pressure to adopt Hindi. I demonstrate that districts with a more
  elastic supply of English skills benefited more from globalization:
  they experienced greater growth in both information technology jobs
  and school enrollment. Consistent with this human capital response,
  they experienced smaller increases in skilled wage premiums.

Human Capital Response to Globalization: Education and Information Technology in India

  India's colonial legacy and linguistic diversity give English an
  important role in its economy, and this role has expanded due to
  globalization in recent decades. It is widely believed that there are
  sizable economic returns to English-language skills in India, but the
  extent of these returns is unknown due to lack of a microdata set
  containing measures of both earnings and English ability. In this
  paper, we use a newly available data set - the India Human Development
  Survey, 2005 to quantify the effects of English-speaking ability on
  wages. We find that being fluent in English (compared to not
  speaking any English) increases hourly wages of men by 34%, which is
  as much as the return to completing secondary school and half as much
  as the return to completing a Bachelor's degree. Being able to speak a
  little English significantly increases male hourly wages 13%. There is
  considerable heterogeneity in returns to English. More experienced and
  more educated workers receive higher returns to English. The
  complementarity between English skills and education appears to have
  strengthened over time. Only the more educated among young workers
  earn a premium for English skill, whereas older workers across all
  education groups do.

The Returns to English-Language Skills in India

Here is a natural experiment that provides an example of the negative effect of the opening of labor markets on the existing population the new workers will compete with (in this case, the arrival of Russian mathematicians on Western mathematicians' wages and job prospects).

  The fall of the Iron Curtain in late 1991 ended nearly 70 years of
  isolation of Soviet mathematicians from the world mathematical
  community. Suddenly free to travel and emigrate, approximately 1000
  Soviet mathematicians, mostly highly productive researchers, relocated
  to other countries. Three-hundred-thirty-six scientists came to the
  United States.

  During the decades of scant contact with foreign colleagues, Russian
  mathematicians, for political reasons that Borjas and Doran explain,
  concentrated on certain fields that tended to get much less attention
  in the West. American mathematicians, meanwhile, had moved ahead in
  areas that Russians largely ignored. When the émigrés arrived in
  America, therefore, they had a great impact on certain mathematical
  fields but much less on others.

  These differential impacts allow Borjas and Doran to compare what
  happened in fields heavily and lightly affected by the influx and to
  analyze what a large infusion of new talent and ideas does to a field
  of research. Combining information from several large databases, they
  track the productivity and affiliations of the Russian and American
  mathematicians. Their inquiry concentrates on two major effects; the
  “knowledge shock” from all of the new approaches and insights suddenly
  available to American mathematics, and the “labor market shock” from
  all the new people suddenly on the American mathematics job market.

  What they found does not support what Borjas calls the conventional
  “rubbing-off” theory, which holds that if “we get all these highly
  skilled immigrants, … somehow there’s a rubbing-off effect that makes
  you and me more innovative.”

  Since the fields with heavy Russian influence had “incredibly bright
  new mathematicians coming in, with all these new theorems, all these
  new techniques flooding the market, you would expect that people
  working in those areas are going to learn quite a lot from them,”
  Borjas says. “At the same time, the number of academic jobs where
  mathematical research is actually done is really not increasing all
  that much. So something has to give.”

  That something, the study found, was the career prospects and
  productivity of many of the mathematicians already here. “When you
  increase the number of very smart people in a field by a substantial
  amount,” Borjas says, “… not everybody benefits. The typical
  pre-existing American mathematician actually lost out.”

  That’s because “a generation of American mathematicians at the very
  peak of their mathematical efficiency by luck just happened to
  graduate at the same time these Russians [were] coming in,” Borjas
  explains. The people of that young generation lost the most. Faculty
  members protected by tenure kept their jobs, but mathematicians who
  had not yet attained it found themselves facing sharply heightened
  competition, which produced an “unprecedented 12 percent unemployment
  rate for new American mathematics PhDs” and “a dramatic decrease in
  the probability of obtaining a position in research universities,” the
  article says. The overall unemployment rate for college graduates,
  meanwhile, was dropping rapidly, from 3.2% to 2.2% between 1992 and
  1996. Many of the young mathematicians in heavily affected fields ended up moving to lower-ranking institutions or leaving academic
  mathematics altogether.

  The situation “drove a lot of American mathematicians into Wall
  Street,” Borjas says. There, in their new role as “quants”
  (quantitative analysts), they turned their talents to inventing
  intricate financial instruments. Ironically, one of Borjas’s
  colleagues has joked, the migration from the formerly communist
  country probably helped to fuel the 2008 economic crisis that nearly
  > brought down American capitalism.
  Taken for Granted: Foreign Invasion


You may find this article useful Licensing and Rent Dissipation.

In simple terms you might think of it as the devaluation of an 'asset' of some sort.


Relevance of change in Inventory in calculation of Free Cash Flow

When you manufacture a product, you incur some production cost. However, you do not account for this production cost in the $\text{Cost}$ line of the income statement until you actually sell the product. While the product is sitting in your inventory waiting to be sold, you have already incurred some (actual, monetary) cost that is not visible in the $\text{Cost}$ line. That is why you have to adjust the reported $\text{Cost}$ by $\Delta\text{Inventory}$ to obtain the actual, monetary cost for the period.

This is why $\Delta\text{Inventory}$ belongs in the FCF calculation.


Is 'full employment' synonymous with 'the natural rate of unemployment'?

Full employment is a more general term, which has much less implication than natural rate of employment. My experience reading a lot of economics papers is that careful authors do not mix the two concepts.

Stricto sensu, full employment should mean 100% of the workforce is employed. But when we are talking about periods such as the trente glorieuses, the term is often used to mean "very low unemployment", which Keynesian economists associate with natural rate of employment. Politicians will most certainly never use the later, while economists may not always be very careful about which one they use.


Yes, there are companies that DON'T pay dividends to their shareholders. Some of these well-known companies are:

Amazon.com Inc (AMZN)
Facebook Inc. (FB)
Netflix Inc. (NFLX)

The main incentive shareholders get by buying at IPO is CAPITAL APPRECIATION.
98% of the time, the company will only be listed on the stock exchange if it has strong financial fundamentals. Thus, a case in which no one buys into that company as soon as it goes public is very rare.
Share prices change due to the changes in the supply and demand of that company's share. Hence, when the company's IPO is launched, a lot of retail as well as institutional investors buy into that stock and increase the demand for it, while the supply stays relatively stable.
This increases the price of the company's share in the short run. However, if the company has strong financial fundamentals its price would continue to grow steadily. But if a company has weak financial fundamentals, the company's stock price may even drop below its IPO price after a few days of its IPO's launch.
Below is the price of Facebook. Inc since its IPO launch. As the company had strong fundamentals it grew into the social media giant it is today.

However, a company like Groupon had a minute increase in its price just for a few days after its IPO launch due to the rapid increase in demand as discussed above. However, because the company did not have strong financial fundamentals, its price gradually started to lower.

So what incentivizes people to purchase a company's share AT IPO?
The very high probability of the stock to jump above its IPO launch price for a few days.
After which, its price is determined by the company's VALUES/ FINANCIAL FUNDAMENTALS
Cheers!


It's the total credit market, not only US government bonds, but also debt of other countries, also corporate debt etc. See here: http://www.investopedia.com/terms/c/credit_market.asp and here: http://www.investorwords.com/8562/credit_market.html

Maybe this chart (not recent though) clarifies it a bit also: http://warisacrime.org/sites/afterdowningstreet.org/files/images/Credit%20Market%20Outstanding%20by%20Sector%2010012008_0.jpg


Literature on DSGE models featuring a financial sector

This is actually one of the more exciting development in DSGE modelling since financial crisis. Including financial sector or friction is definitely a growing area of research and interest.
Unfortunately its quite a new development so there are not that many papers yet out there (however if you consider doing research in this field that also means there is a lot of untapped opportunity to do work there). You will probably want to read most of the works of Gretler who is one of the pioneers in this area.  Gertler and Kiyotaki (2015), Gertler and Kardi (2015) and Gertler, Kiyotaki, and Prestipino (2016) are good texts.
Next papers such as Christiano, Motto, and Rostagno (2014) are looking at financial sector in a bit roundabout way but I think can be still considered to be part of this research area.
Lastly, this paper from Christiano, Eichenbaum, and Trabandt is a nice literature review on DSGE modeling since Great Recession. It covers the above mentioned papers plus also some others. It is a general review that looks also on other advances in DSGE modeling and also at their criticism but I would say its worth reading nonetheless even if you are only interested in the financial DSGE models.
You will also find more sources by looking at papers cited in the literature above.


Based on the information you have given, both the solution manual and your solution are wrong. You are correct that the fixed cost for system I is 1. Variable cost is given by $$VC(x)=TC(x)-FC.$$ The variable cost for system I is therefore equal to $0.9x$. Note that the variable cost depends on $x$! For system II the variable cost is $0.1x$.

You appear to have confused variable cost with marginal cost. Variable cost is the part of the total cost that changes with quantity; marginal cost is, roughly, the cost of producing one more unit (and is given by the slope of the total cost curve). Thus, the marginal cost of system I is $0.9$ and the marginal cost of system II is $0.1$.


Pay What You Want Pricing; Micro Foundations

It seems that some consumers get utility from paying for the good.  This isn't always true, and it does seem counter-intuitive at times.  In fact, it often seems that consumers get utility from getting a "great deal," and we often hear them brag about their amazing feats of 80% off.
In this case, however, for a few different reasons, some consumers get utility for paying.  It is the responsibility of the seller to try to maximize or enhance the utility for paying in order to increase revenue.  They have a few tools to do this, including making a big deal about how some of their proceeds may go to charity and by thanking "viewers like you" who choose to pay more than $0 for the good in question, and whose contributions allow the producer to continue operating.  They may also provide Internet Points in much the same way that that Stack Exchange and other sites do in order to help incentivize suppliers to provide quality content in exchange for zero dollars.

They can also be thought of as providing disutility, using some of the same tools, to those who don't pay.

And, as you mentioned, sellers often go with a PWYW scheme to improve market penetration, and this is often stressed.  Sellers who need market penetration can typically pass as "indie," and flaunting their indieness may again improve the utility the consumers get by choosing to "support" them, the "little guy" or underdog.  This brings to mind a couple of economic papers (which I can't seem to find at the moment) relating to horseracing and how it seems that people get utility from betting on the underdog to the point where it was once possible to have a positive expected value for betting on the favorite whenever the odds were above somewhere around 1:1 or 1:2.
Anyway, marketplaces for these goods tend to strongly emphasize this, doing whatever they can to promote their sellers as home-grown upstart startups, even putting it in their respective names, encouraging consumers who pay to think of themselves as supporters or patrons rather than consumers or even foolish rubes who paid for something that they could have gotten for the sum or $0.

To summarize, I think that those who choose to pay are actually purchasing the good in a bundle: the item being sold, along with some quantity of warm fuzzies.  At a certain point, consumers either get no additional fuzzies or start getting anti-fuzzies (cold stabbies?  Ice bricks?) for paying any more.  Some consumers get no fuzzies for paying, or their willingness to pay for fuzzies is low, so they pay zero dollars. Sellers know this, so they are responsible for maximizing the quantity of fuzzies (perhaps by influencing consumers) and gaging how many consumers they can expect to get fuzzies before deciding to use PWYW.  It's possible that the quality of the good being sold is related to the quantity of fuzzies that the buyer receives.  That is to say, consumers may enjoy paying parties who make "good" goods, and they will likely feel ripped off if they voluntarily pay for a "lemon."


marginal utility of consuming vs marginal rate of substitution

This is because the question is asking the MRS of soda for hotdogs not the MRS of hotdogs for soda.

i.e

$MRS(MU(soda),MU(hotdogs))=MU(soda)/MU(hamburgers)= 2/10=1/5$ To get 5 you need to solve the MRS of hotdogs to soda $MRS(MU(hotdogs),MU(soda))=MU(hamburgers)/MU(Soda)=10/2=5$

Hope this helps.


Nash Equilibria in Target Destroying-Guarding Game

Excuse the formatting for the game's normal form. This is a zero sum game, so I've written only the payoffs for the attacker:

$$\begin{pmatrix}
    A|D & a & b & c\\
\hline
a & 0 & v_{A} & v_{A}\\
b & v_{B} & 0 & v_{B}\\
c & v_{C} & v_{C} & 0
\end{pmatrix}$$

Evidently, since $v_{A} > v_{B} > v_{C}$, there may be payoffs such that there exists some $\lambda$ such that $(1-\lambda)v_{B} > v_{C}$ and $\lambda v_{A} > v_{C}$.

First, suppose that the parameters are such that there exists such a $\lambda$ e.g. $v_{A} = 20$, $v_{B} = 10$ and $v_{C} = 1$. In this case, $c$ is strictly dominated and so we can eliminate $c$ from the support of the attacker's mixed strategy, and hence we may also eliminate $c$ from the support of the defender's mixed strategy. Write the new matrix as

$$\begin{pmatrix}
    A|D & a & b\\
\hline
a & 0 & v_{A}\\
b & v_{B} & 0\\
\end{pmatrix}$$

Hence, the unique mixed strategy equilibrium is $p := \Pr(a|A) = \frac{v_{B}}{v_{A}+v_{B}}$ and $q := \Pr(a|D) = \frac{v_{A}}{v_{A}+v_{B}}$. Note that as the value of target $A$ increases, the attacker will actually attack it more infrequently.

If, $c$ is not dominated (strict or weak) then the algebra becomes more tedious, but the unique equilibrium will be a mixed strategy with full support over all three pure strategies. I leave this to you.

Finally, I leave to you the case in which $c$ is only weakly dominated. Hint: In general there can be weakly dominated strategies in support of a mixed strategy equilibrium, but if the opponent's mixed strategy has full support, there cannot be.


Applications of Optimal Transport in Economics

Optimal transport methods are very much still in use in economics. The show up in two-sided matching with side-payments, contract theory, hedonic pricing, partial identification in econometrics, and a couple of other areas. You can find an excellent overview of economic applications in the 2016 book Optimal Transport Methods in Economics by Alfred Galichon.


Trying to show result of prisoners dilemma simulation through supply/demand graph

All right, first some initial thoughts:

You don't really need game theory for this, although you could.
Take it to a multi-person level and basically what you have is
simple competition.
Unless there is some other motivation to
take the job (for example because one is required to do an
internship and it's one of the only ones offered) you would not
compete to 0, but to your reservation wage, below which you would
refuse to work at all. Of course you can always say that you've
normalized the reservation wage to 0. Moreover, the reservation wage is not the same for everyone because we have different alternative opportunities, typically.
If there is an other reason to take the job, as e.g. with the earlier mentioned internship, there is no reason why competition could not drive the wage below 0, that is people will actually pay to take the job if they want it bad enough.

As for your question in principle nothing happens to demand (in the short-run); only the quantity demanded changes. Suppose before the game is played we have the initial situation like this:

The staircase function is the supply of laborers, the dotted function the demand. In this situation person 1 is willing to work at wage=1, and person 2 at wage=2. In principle in this case the firm would hire person 1.

Now the game is played and person 2, undercuts person 1, who will undercut person 2 in turn etc.. What this does is bring the staircase steps down until they are all aligned at 0 like this:

The supply curve becomes a horizontal line at 0. The quantity demanded for the number of laborers changes from 1 to 2.

In a model involving more people that all have the same reservation wage the supply curve would look like a straight upward-sloping line and competition would induce the supply curve to turn until it is horizontal at the reservation wage.

Why does that not happen in real life? Given that typically everyone has not the same reservation wage because they have different outside options, people do drop out of the competition earlier and the supply curve stays an upward sloping line.


In the United Kingdom, your death bonds would be prohibited by the Life Assurance Act 1774, which still says in its entirety:

  Whereas it hath been found by experience that the making insurances on lives or other events wherein the assured shall have no interest hath introduced a mischievous kind of gaming:

  From and after the passing of this Act no insurance shall be made by any person or persons, bodies politick or corporate, on the life or lives of any person, or persons, or on any other event or events whatsoever, wherein the person or persons for whose use, benefit, or on whose account such policy or policies shall be made, shall have no interest, or by way of gaming or wagering; and every assurance made contrary to the true intent and meaning hereof shall be null and void to all intents and purposes whatsoever.
  And it shall not be lawful to make any policy or policies on the life or lives of any person or persons, or other event or events, without inserting in such policy or policies the person or persons name or names interested therein, or for whose use, benefit, or on whose account such policy is so made or underwrote.
  And in all cases where the insured hath interest in such life or lives, event or events, no greater sum shall be recovered or received from the insurer or insurers than the amount of value of the interest of the insured in such life or lives, or other event or events.
  Provided always, that nothing herein contained shall extend or be construed to extend to insurances bona fide made by any person or persons on ships, goods, or merchandises, but every such insurance shall be as valid and effectual in the law as if this Act had not been made.

and there are similar laws in other places.

There were two bases for this prohibition and regulation:

it amounted to a particularly distasteful form of gambling on people's deaths
it was dangerous for an individual to be more valuable dead than alive to another individual; insurance should solely be about compensating for losses

Housing loans are not as distasteful as human death, and there is no suggestion that purchasers of Credit Default Swaps could have acted in a similar way to murderers, though the reference in section 3 to events might also apply to their use for speculation


Marginal product of capital net of depreciation

This is a misuse of terms.

The marginal product of capital net of depreciation is indeed $MP_k - \delta$, there is no doubt about it.

What the authors mean  is that

$$R = 1 + (MP_k - \delta)  $$

is the "gross return on capital" factor. In fact a few lines below the equation they call it exactly that, which is the correct term. Here "gross" reflects the inclusion of $1$.


model design - fixed effects model for paired differences

The constant term in your final FE model has no specific meaning without further restrictions. For Stata, it is only chosen such that the (sample) mean of the estimated individual effects add up to 0. So your testing $\alpha>0$ is in fact based on
$$\frac{1}{n} \sum_{i=1}^n \hat\alpha_i,\quad \hat\alpha_i = \frac{1}{T_i} \sum_{t=1}^{T_i} (x_{it} - y_{it}),$$
where I am using the $T_i$ notation in order to allow for the panel data to be unbalanced.

For balanced panel data, the test is (almost) identical to one based on OLS if you use a cluster standard error for OLS because then
$$\frac{1}{n} \sum_{i=1}^n \hat\alpha_i = \frac{1}{n} \sum_{i=1}^n \frac{1}{T} \sum_{t=1}^T (x_{it}-y_{it}) = \frac{1}{nT} \sum_{i=1}^n \sum_{t=1}^T (x_{it} - y_{it}),$$
where the LHS is a statistic from the FE regression and the RHS is the corresponding one from the OLS regression. OLS, FE and RE (and BE) will all give an identical intercept estimate, I think. Only standard errors will be different. If the panel data set is unbalanced, the method using the FE regression and that using the OLS regression differ in how the average differences ($\hat\alpha_i$) are weighted across different $i$.

The test you mention is about whether the mean of $x_{it}-y_{it}$ is bigger than 0 or not. This looks OK. What else? Which technique to use (OLS or FE or RE or BE) doesn't seem to be critical here. I would just use OLS + cluster se, as it is simple to both the researcher and the reader. Try:

xtset id year
gen d = x - y
reg d, vce(cl id)
xtreg d, fe
xtreg d, re
xtreg d, be


Russian ruble drop in value due to low oil prices

The Rouble depreciates against the US dollar because of demand and supply.

Think of it as a story that goes as follows:

a) In the ruble/dollar market, the supply of dollars consists of foreigners that have dollars and want to buy goods(oil and other things) and assets from Russia, while the demand for dollars consists of Russians that want to buy goods and assets from foreigners and need dollars to do so.

b) When the price of oil collapses 50%, then the foreigners only need to supply 50% as many dollars to buy all the oil they used to buy from Russia. So supply of dollars to the ruble/dollar market shrinks. When supply shrinks, prices increase.  Imagine that as the Russians want to buy foreign goods, say medicine, and they will be willing to pay lots of Roubles to buy said medicine, so demand for dollars does not change as much as supply of dollars.


Gali (2015) Chapter 8 Symmetric Steady State

The steady state is "symmetric" in the sense that terms of trade with all other countries are equal to $1$, with respect to the home country. It would be non-symmetric if for some of them terms of trade were not equal to unity.

Is there anything in the model that precludes this steady state from existing? Usually not. Then it is feasible, i.e. it exists. Whether it is an attractive, stable steady state, is another matter.

$S$ is the ratio of two price levels. Logically then, it is a derivative measure of these two, so the price levels are the underlying forces.


Difference between double auction and continuous double auction?

So, if a distinction is made, as continuous double auctions are usually just called double auctions, then the difference has to do with frequency.  It is easier to have an example.

The New York Stock Exchange is an example of a continuous double auction.  Within its trading hours, you can bid in either direction as much or as often as you want and so can everyone else.

The best example of an ordinary double auction was the Arizona Stock Exchange which closed in 2001.  It only permitted two bids per day, at the open of US trading and at the close.  It sought to solve an institutional liquidity problem.  Certain types of institutions are at risk if they cannot clear their orders at or as near to as possible a particular moment in time.  Mutual funds, for example, usually only have one valuation per day and if there need to be a lot of transactions from redemption or purchase activity, then the least risk is to have the trade near that time.  Most are at the open or close.

The Arizona exchange found the single price that cleared the largest number of trades.  You paid a fee to participate.  There was no bid-ask spread.  So possibly hundreds of customers would place their limit orders at the same time as buyers or sellers.  What they did in practice was create single supply curve and a single demand curve and the price paid/received was the intersection of the two curves.


How do I change the base year of real GDP using the GDP deflator and nominal GDP?

It's just real GDP in year t, times (deflator in year 2000 / deflator in year t).


Financial integration and connection between savings and investment

Suppose that you cannot trade capital. I.e., you cannot ship savings from one place to another. In that case, if region A saves 10, region A will invest 10. If region B saves 1, it will invest 1. So, from that example, you can conclude that if markets for capital are closed, the correlation between savings and investment is 1. For completeness, now imagine the free flow of capital situation: region A saves 10, region B saves 1, but it is in region B where all the investment opportunities are, so the regions trade in capital: A invests all its savings in B, and investment in B is then 11, and investment in A is 0. In this second example I and S are therefore negatively correlated ...


Do the stock performances of various companies have a measurable correlation with exchange rate?

A company valuation is merely in paper. It is similar to  economist eating shit jokes. When the stock price of a big company drops, it "suppose to" hurts the shareholder who bought it on high.

When I say "suppose", because intervention does happens to "too big to fail" company. In worst case, government hold too much "interest" in big company that might issue bonds(AKA print money) to bails out the entity, as what happens to Asian financial crisis, and USA subprime loan financial crash.

IMHO, details real world example falls under TL;DR; category. There is countless global example.

A large company stock can be jack up by speculation. Say a company issues  10 millions stock with $1 face values. If a willing buyer trade 100 stocks with $1.50, and subsequent buyer keep buying 100 stocks and jack it to $50, it doesn't mean the company "values" sudden shoot up from $1 x 10 millions = $10 millions  to $50 x 10 millions($500 millions). This WILL NOT create extra $490 millions to the economy, nor to the money supplies.

Without government money supplies and cheap loan rates, it is impossible to create such speculation. In addition, without increase money supplies, not hot money flow into a country, foreign exchange will always stay in a stabil level.

Example of high stock values and insane inflation

Zimbabwe stock market compare stock value.

Examples of bubbles crash and NO bailout made, no apparent inflation

Dot Com bubbles.
Enron, Worldcomm scandal.

Example of bubbles crash. Country bail out cause higher inflation

Asia pre 2007 economy crisis stock value

Example of crash and bail out inflation flow to other country.

USA QE. Speculator borrow cheap money and speculate outside US. Hot money cause many Asian country company stock shoot up without fundamental.


Lemma 2 in Tirole and Maskin's Dynamic Oligpoly I (1988)

The monotonicity result that they prove in their lemma 1 (p. 556) is stronger than you think: it states that whenever $q>q'$, the support of $R_1(q)$ is "uniformly below" the support of $R_1(q')$. In other words, it is impossible to find any realizations $(r,r')$ of $R_1(q)$ and $R_1(q')$, respectively such that $r>r'$.

The proof of this result relies on the idea that firms decisions are strategic substitutes in the Cournot duopoly model: the more firm 2 produces, the less firm 1 wants to produce. Thus, if firm 1 finds it optimal to produce an amount $r'$ with positive probability when firm 2 produces $q'$, it cannot find it optimal to produce a larger amount ($r>r'$) when firm 2 itself produces more ($q>q')$.

Consider therefore a production level $q$ by firm 2 such that $0$ is a realization of $R_1(q)$. Take $\epsilon>0$ and suppose that there exists $r>0$ that is a realization of $R_1(q+\epsilon)$. The condition
\begin{equation*}
r > 0 \text{ and } q<q+\epsilon
\end{equation*}
violates their lemma 1, since $r$ is a realization of $R_1(q+\epsilon)$ and $q$ is a realization of $R_1(q)$. This proves that $R_1(q+\epsilon)$ has all its weight on 0.


It is the first one, $TC(0) = FC$. This is the definition.

Also consider that it is not clear what is "transformed by $q$ in some way". In case of
$$
\frac{5q}{q+1} + \frac{5}{q+1}
$$
are the two fractions transformed by $q$, or should I just sum them up to 5?

With your function, one can rearrange it to
$$
TC(q) = \frac{5}{q+1} + 5 + 5q + q^2 = -\frac{5q}{q+1} + 10 + 5q + q^2.
$$


How to achieve the best outcome by a single statement in this game?

A caveat, this is not really an answer, more of an extended comment--that's why I've put it as community wiki. This is a cool game, one of commitment.

I'll try to formalize the problem. This is a first pass, so people should edit as they see fit.

It's probably easiest to solve the case where, $T$, the number of rounds before a forced execution is $1$. Let each player get $1$ if they shoot someone else and $-1$ if they are themselves shot.

There are two stages of the game, the commitment stage and the shooting stage. The sequential stage is (fittingly) sequential move and the commitment stage is not really a game persay, since the players are automata (their strategies are predetermined). I'll return to this shortly.

An easy result: there is an equilibrium in which rules 2 and 3 are rendered superfluouous since each player can commit to every possible contingency.

There is a source of exogenous randomness, a random state of the world $\Theta = \Theta_{1} \times \Theta_{2}$, $i = 1,2$, where $\Theta_{i}$ is the set of permutations of $\left\{A,B,C\right\}$. These are the possible orders in the shooting portion.

The set of actions for player $A$ is $\mathcal{A} = \mathcal{A}_{1} \times \mathcal{A}_{2}$ where $\mathcal{A}_{i} = \left\{B,C,\emptyset\right\}$ for $i = 1,2$ (corresponding to "shoot $B$," "shoot $C$," and "shoot nobody," respectively). The sets of actions for the other two players, $\mathcal{B}$ and $\mathcal{C}$, are defined analogously.

A (mixed) commitment strategy of $A$ is a mapping $$\sigma_{A}: \Theta \times \Sigma_{B} \times \Sigma_{C} \to \Delta\left(\mathcal{A}\right)$$
where $\Sigma_{i}$, $i = A, B, C$ is the set of all (feasible) mixed strategies. An example of an infeasible strategy for player $A$, say, is one that does not have $\emptyset$ played with probability $1$ when the state and mixed strategies of the other players are such that $A$ has been shot for certain.

Note that I am using the fact that the shooting portion of the game can be viewed as a instantaneous (random) outcome of the commitment portion, which is the real game.

I'm not quite sure about the commitment strategies for $B$ and $C$, due to the sequential move nature of the commitment stage. I suspect that that does not matter, and that they are defined analogously. I'm not completely sold on this and open to being convinced otherwise.

Each player's payoff is defined in the obvious way.


What idea was proposed to remove global poverty?

Unlike many Nobel prizewinners, this year's award was not really given for a central theoretical development. All three of the winners have made important theoretical contributions relevant to development economics and that inspired their future work (for example Banerjee on networks or Kremer on technological change and productivity). However, their major contribution, and what they received the award for, is their major methodological contributions.

In particular, the winners are responsible for designing and enacting a number of large-scale field experiments in development economics. These experiments use theory and domain knowledge to break poverty down into a series of more manageable problems, and then attempt to actually alleviate one of those problems with a policy intervention. These interventions are administered in randomized ways that allow us to see whether they actually work (like incentives to reduce teacher absenteeism, they found) or not (microfinance, they found).

Through these field experiments they have improved the lives of millions of people in at least some small way, and their work has led to huge changes in the way that development economics operates as a whole (for better or worse, I think better). The field today is much more focused on experimental and other causal work than it used to be, using data and empiricism to answer questions about development economics rather than using theory. In many cases this work focuses on finding actually-effective ways to improve policy and conditions in developing countries.

You can see a more thorough description here: https://marginalrevolution.com/marginalrevolution/2019/10/the-nobel-prize-in-economic-science-goes-to-banerjee-duflo-and-kremer.html


How does a govt surplus increases investment?

The increase in savings you described does happen, in the short-run. Remember, tho, investment and savings are flows. Accounting identities provide us truths about their ex-post behavior, not planned or desired savings.


Why wouldn't an arbitrageur who shorts an overpriced security, and longs a substitute company, break even?

Yes, you've understood half the reason for taking the joint position: if sector-wide movements occur, then the position stays neutral: i.e. it is insulated from those movements.

It's not sector-wide movements that this position is trying to profit from. So it's wise to insulate the position from those movements.

What this position is trying to profit from, is the relative over-pricing of the shorted-security, and the relative under-pricing of its competitor. If the arbitrageurs are correct, and can stay solvent long enough, then each of their two positions will make a profit when the shorted security drops in price, and the held security rises in price.


Efficient social allocation awards item to the highest valuer in a sponsored search auction. Whom is it optimal for?

The optimality concept used here is usually Pareto-optimality, a.k.a. Pareto-efficiency.

  Pareto efficiency or Pareto optimality is a state of allocation of resources from which it is impossible to reallocate so as to make any one individual or preference criterion better off without making at least one individual or preference criterion worse off.

So a Pareto-efficient allocation is not necessarily 'optimal for' anyone. It just means that given the current allocation no one can be made better off without cost to others.


It just arrived to the Swiss Central Banks. Its assets were depreciated of $50 billion in six months.
As fortune summarized:

  Luckily for the Swiss National Bank, it can’t actually become insolvent, since it can simply print francs to cover any losses it suffers. The announcement caused the value of the Swiss Franc to tumble, falling as much as 0.4% against the euro on Friday.

If the ECB would lose money because its assets'value is decreasing, the ECB would print money as well.

By the way, there was an article in voxeu from Paul de Grauwe and Yuemei Ji which title was "Why the ECB should not insist on repayment of its Greek bonds":

  It would be easy for the ECB to solve this problem. The easiest way
  would be to write off the Geek bonds. As we have argued earlier such a
  write-off has no fiscal implications. It means that the circular flow
  of interest from Greece to the ECB and back is discontinued. In
  contrast, the repayment of the bonds by Greece has fiscal implications
  and leads to transfers from Greece to the other member countries. This
  is quite a perverse transfer given the sorry state of the Greek public
  finances.

I am giving you the advise to read the article from de Grauwe and Ji really. I think it would answer most of your questions.


What are the drivers of the price of land?

Because of population growth, savings, and technological innovation the stock of capital and augmented labor are growing over time. These forces raise the marginal product of land. Unlike capital, the supply of land is fixed. This means that the supply of land can't increase in response to the rising marginal product. Investors have to be indifferent between investing a dollar in holding land and a dollar of productive capital. Capital is paid its marginal product ($r$). For investors to be indifferent, each dollar buys $1/P$ worth of capital, and that fraction of a unit of land returns $r$. One unit of real estate actually returns $rP$. So, over time, the forces that raise the marginal product of labor and capital, population growth, capital deepening, and technological innovation, raise the marginal product of land and therefore push up the price because the quantity cannot adjust.

This assumes we can abstract from issues of taxation and depreciation. We can handle those details but they don't change the basic story. What is more important issue is that land is in fixed supply and capital is not.

There are some innovations that increase the literal or effective quantity of land: e.g. landfill, multi-story construction, enhancements in in agricultural productivity. More accurately, in practice, the quantity of land can adjust can adjust in a limited way, more slowly, and at greater cost than physical capital. This preserves insight above: quantities do not adjust to equate the marginal product of physical capital and land, and so the price must do so instead.


What is the definition of "First Best", "Second Best", etc. in contract theory?

In contract theory

The first-best refers to the best you could do if you knew agents' preferences over labor an income (i.e., if you did not have to impose the incentive compatibility constraint), and the second-best is the best you can do if agents have to reveal their preferences themselves.

In mechanism design

A useful reference is Galichon, Alfred, Ex-Ante vs. Ex-Post Efficiency in Matching (May 21, 2011). Available at SSRN: http://ssrn.com/abstract=1837321 or http://dx.doi.org/10.2139/ssrn.1837321  :

  "an assignment is called ex-post efficient if no other deterministic assignment is improving on it in a Pareto sense; and ex-ante efficient if no lottery over deterministic assignments is." (my emphasis)

Difference between the two

There is not much connexion between the two notions as defined above. Every combination of the two notions is a priori possible. Both a mechanism and a contract can be

First-best ex-post efficient (i.e., efficient when incentive compatibility constraint is not imposed and the outcome of the mechanism/contract must be deterministic)
First-best ex-ante efficient (i.e., efficient when incentive compatibility constraint is not imposed and the outcome of the mechanism/contract can be random)
Second-best ex-post efficient (i.e., efficient when incentive compatibility constraint is imposed and the outcome of the mechanism/contract must be deterministic)
Second-best ex-ante efficient (i.e., efficient when incentive compatibility constraint is imposed and the outcome of the mechanism/contract can be random)

In the literature

Yet, in general, you are be more likely to find the first-best/second-best terminology in contract theory (random contracts are not that common in contract theory), and the ex-post/ex-ante efficiency in mechanism design (knowledge of the preferences is rarely assumed in a mechanism: the fact that we do not know agents preferences is the raison d'être of mechanism design).

Thus you can expect to see 3. and 4. being discussed in the mechanism design literature, and 1. and 3. being discussed in contract theory.

Beware

This being said, the notions of second-best and first-best are often used outside of contract theory in a rather permissive way, which may be confusing.

For some people, first-best simply means "if we do not impose some constraint $X$" and second-best means "if we do impose constraint $X$".

Thus you may hear people talk about (beware this is where it gets confusing)

an ex-post efficient mechanism as "second-best" efficient, because it is "efficient under the constraint that the assignment be deterministic".
an ex-ante efficient mechanism as "first-best" efficient, that is "efficient if we do not impose constraint $X$".

Hope that clarifies it (or at least does not confuse you more).


What is the difference between VAT and corporate tax

Both taxes take a cut from the difference between the income of a
  company and its expenses.

Wrong. Companies collect VAT as though they were tax-collectors on behalf of the state. This is why the amount of VAT that a company pays to its suppliers' is subtracted from the VAT that the company has collected from its customers, and only the difference is paid to the State.

As to whether they are "good" or "bad" taxes, and which is which, as usual it is a complicated matter (and when you read/hear such assertions, make your eyes or ears walk away), a matter that has to do not only with businesses but with the macroeconomy as a whole, while it also depends on the current state of the business cycle, whether there is a depression, etc.

ADDENDUM
Responding to the comment by the OP to this answer, here is a schematic view of the matter

V.A.T is not part of the Revenues/Sales of a company. It is a tax imposed on consumers, and collected by the company on behalf of the state.

Imagine a situation without V.A.T., i.e. assume that currently only the first column of the above exists. Then V.A.T. is enacted into tax law. The Profit and Loss statement of the company will not include it, neither in the Revenues side, nor on the Expenses side, it remains unchanged. The amount collected from customers goes up, but the Revenues of a company don't. And if among the company's customers are other companies, they will not see VAT as an increase in cost, because they will get to offset what they pay with what they collect from their customers. In other words, V.A.T is a tax imposed on final private consumers, and everybody else is involved as a tax-collecting entity only. Note that V.A.T is charged and collected irrespective of whether a company has profits or losses.

In a very general sense, all taxes are "equivalent" in the sense that they have the same goal: bring part of output under the ownership and control of the state. But it matters greatly how do they affect the "before-the-State" economy. In this particular case, V.A.T makes products more expensive for final consumers, and it is about how they will react to these increased prices. On the other hand, Income tax cuts directly from a company's profits, if profits exist, and so it affects the incentives of businesses to go into business.


Utility Function Implies Consumption of Not All Goods

There might be a systematic way (e.g. some variation of the gradient method), but it is probably not trivial (see here), unlike this problem.

By checking $U(j,k,0)$, $U(j,0,s)$ and $U(0,k,s)$, you will find that one of them can actually never occur when utility is maximized. Then you can move on to examine the two goods where 0 consumption can occur, and compare their marginal rate of substitution to their price ratio. Whenever these are unequal in optimum, it signals a corner solution.


Understanding the use case of farming crickets in developed countries

I think there are probably a few different answers to your question, all of which likely play some role.

First, in many developed countries, insect use in human food is regarded not as an inferior good, but a normal good. By highlighting things like nutrition content (specifically protein), they're trying to capture a market that includes more affluent, health conscious consumers. These individuals are likely to be a bit less price sensitive, and may even use price as a signalling component. Therefore, products using crickets might be less sensitive to pricing compared to other goods.

Second, companies trying to sell cricket-based products might also feel that their consumers would be more concerned about ingredient sourcing to distant farms. This could be because of perceived fears of quality differences, or because of things like a preference towards local sourcing.

Third, it seems that many of the newer farms opening in the developed world like the one profiled here are addressing the issues of labor intensity in cricket production by increasing efficiencies and utilizing more technology in the production process.

There's also likely some natural home bias in products like this. Much of the grassroots support for increased cricket consumption is driven by the farmers themselves. Because stronger community roots can improve these efforts, and because of the high costs of researching, incorporating, and offshoring production facilities to a distant country, there might just not be that much of an economic incentive to move production overseas. It isn't trivially easy to offshore an entire production facility, and the profitability and long term market potential might not have quite reached a position where that's profitable yet.


Assuming the present is taken to be period 0, your approach is correct, subject to one caveat.
As you suggest, the internal rate of return is the discount rate at which the discounted present values of all the costs and all the benefits of a project sum to zero.  In this case, you are given the stream of benefits, and the right hand side of your equation correctly represents the discounted present value of that stream.
So far as the left hand side is concerned, instead of being given a stream of costs to treat in the same manner, you are just given the net present value of costs.  If the question is from a textbook and that is all the information given, you can hardly do anything else than use that net present value.
The caveat is that it's unclear how that net present value of the costs has been obtained. If it was calculated using any discount rate other than the true internal rate of return, eg an organisation's standard discount rate for evaluating the net present value of proposed projects, then (except by coincidence) your approach will not give the correct internal rate of return (and the given answer will be wrong).


What would happen if the US defaults on its debt?

The Russian default was an extraordinarily insignificant event compared with a current-day US default on all treasury securities.  There are no remotely similar events to compare it to.  This makes it hard to make guesses about all the ramifications.  Still, speculating is fun.

My guesses, based on a complete default (no money recovered) on all treasury securities:

Massive failure of important financial institutions all across the world.  LTCM was a hedge fund: a type of institution the is specifically expected to have significant failure risk.  On the other hand, treasury securities are held by institutions that are not well-equipped to bear risk.  Pension funds, banks, central banks, possibly whole governments would immediately fail.  There would be ripple effects as other institutions linked to them (or who they owe money to) faced an uncertain future.
Total freezing of most important financial transactions and many markets.  Treasury securities are used as currency and margin in many large and important financial transactions.  We witnessed a great deal of hobbling of financial institutions and markets when a few CDS's and mortgage-backed securities failed.  The loss of confidence in financial institutions and markets would be incomparably greater.
Drastic increases in all interest rates and a sharp decline in all stock prices.  The Russian default had consequences because it caused investors to dump risky investments in favor of safe ones.  A failure of the safest investment (treasuries) would cause utter loss of confidence in all investments and investors wouldn't know where to put money.  Cash maybe?  They would pull money out of both safe and risky investments, driving down both stock and bond prices.
Widespread unemployment. Cash available for lending would dry up.  Firms would be unable to borrow and have to forgo projects and lay people off.  These people would reduce their spending and default on some loans, causing amplified effects on interest rates and economic activity.  I would not be surprised if starvation and loss of social order ensued.
Irreparable harm to confidence in the US government.  The government would never again be able to borrow at low rates, it may not be able to borrow to fund its usual deficit.  This would be problematic since the depression and economic turmoil would require expanding the deficit.  It would be my guess that the US government as we know it would not continue.

I know this sounds a little alarmist, but wholesale default on all US government debt is truly an end-of-the-world-as-we-know-it scale event.    The world truly would change dramatically. Probably future generations would read about the golden age of humanity that ended with the great default.

Realistically, the US is not going to completely default on its debt any time soon.  If there was even a bit of worry about it, people and institutions would stop using it as such a critical component of their transactions and saving.  This would also be a dramatic change, but it would happen over time and reduce the effects, should a default ever happen.  I'm not sure what people would use as a substitute.  It would be uncharted territory.

A more likely, but still apocalyptic, scenario would be some partial default or sustained high inflation.  See the above consequences, but scaled down and including loss of confidence in the dollar.


“So if I go to an arcade, and I spend money in it playing videogames, I'm growing GDP?”

Yes this is correct.

“Is there any metric to measure the creation of products and services needed to sustain and expand life?”

The national accounts reported by most statistical offices are often subdivided into different sectors such as agriculture, industry etc. if you would want you could exclude entertainment industry but nobody is doing that as an official separate measure because in economics we care about all humans needs not only the most basic ones like shelter etc. and entertainment is actually an important human need. However, you are free to make any measure of “progress” as you define it from those separate accounts. You can for example check gross product or gross value added by sector for European countries at Eurostat website.


What is the actual paradox in the Paradox of Thrift?

The Wikipedia article has several different quotations over time, though I would state the claimed paradox as a variant of (2), possibly something like

  an increase in the rate of saving can lead to lower total savings

perhaps illustrated with a graph something like

which suggests that the paradox seems to depend on the the combined relationships between income and savings and between investment and output, together with some macroeconomic tautologies.  If the relationships were different, or if the tautologies did not cause changes in equilibria, there might not be a paradox


If you are looking at US producer prices then the Bureau of Labor Statistics is where you would go to find the data.  Producer price indexes are here: https://www.bls.gov/ppi/

If you are looking at other countries producer prices then I would recommend  the OECD:  https://data.oecd.org/price/producer-price-indices-ppi.htm

Or this site as well:  https://www.economy.com/indicators/producer-price-index-ppi


To use seasonally adjusted data or not seasonally adjust data?

There is a theoretical objection to using seasonally adjusted data, which I saw in an essay by Kalman (developer of the Kalman filter); sorry, I cannot find the reference. The argument is that seasonal adjustment is a filter, that has its own dynamics. You are then embedding these filter dynamics within the system that you are modelling. This would be a bad idea in engineering systems, but I am not entirely convinced it matters for economic systems. (My academic background is in engineering control systems.) However, if the variables you are forecasting exhibit roughly the same seasonal pattern, separately seasonally adjusting them might be problematic.

Most economic forecasting exercises involve variables with different seasonal patterns, and it is probably easier to work with seasonally adjusted data. You would have to embed the deseasonalisation within your model, which raises model complexity, and probably introduces extra errors that we know that we can explain.

If your objective is to forecast the non-seasonally adjusted variable, the usual practice in finanicial market economics is to come up with an estimate of the non-seasonally adjusted variable, then apply the existing seasonal pattern. The main example I have in mind is the forecasting of the CPI index in the inflation-linked markets, where the instruments are linked to the non-adjusted index. These forecasts were short-term (few month horizon), and were expected to be fairly accurate. Even long-term forecasts had a seasonal pattern built into them, since it mattered for relative value even for instruments with longer maturities.

Academics may prefer other techniques, but I am unaware of their use in short-term inflation forecasting.

If you are attemptng to forecast an aggregate index by forecasting its components, you might need to forecast each component, and then aggregate and seasonally adjust using the same algorithms used by the statistical agency. However, it’s rare that you can get the level of accuracy needed for this to matter.


Why intuitively does Break-even point $= \dfrac{\text{Total fixed costs}}{\text{Selling price per unit} − \text{Variable cost per unit}}$?

Intuitively, the breakeven point is where the volume of sales results in the gross margin (based on the difference between selling price and variable costs per item) exactly covering fixed costs


Annual data VS Monthly data VS Quarterly data for a VAR model

For example, the response of $y_t$ to a shock in $x_t$ should be expected to be different at monthly, quarterly and annual frequencies, right?

Generally (and usually), yes.

Or it is that the results should be similar irrespective of the frequency of the data?

Generally (and usually), no. The answer to your question depends on the underlying data generating process (DGP). You can take an example process and analyze it at different levels of time aggregation analytically to figure out implications for what happens when we look at it using different frequencies. E.g. take a monthly AR(1) process
$$
y_t=\varphi_1 y_{t-1}+\varepsilon_t
$$
and see how it behaves when aggregated to quarterly or annually:
$$
\sum_{i=1}^{3}y_{t-i+1}=f\left(\sum_{i=1}^{3}y_{t-3-i+1}\right)+u_t
$$
and
$$
\sum_{i=1}^{12}y_{t-i+1}=g\left(\sum_{i=1}^{12}y_{t-12-i+1}\right)+v_t
$$
where you need to figure out $f(\cdot)$ and $g(\cdot)$ based on the original AR(1) model.


How can two countries of roughly the same size, population and GDP have such big differences in minimum wage?

The minimum wage is usually the outcome of a bargaining process between the government, businesses (via lobby), and the trade unions. As such, a key driver of the final outcome depends on the balance of power. In countries like France and Germany, with strong unions in the former, and wide collective bargaining on the latter, the minimum wage is relatively high, compared for instance with Poland. Meanwhile, in Poland the minimum wage is roughly two times that of Chile, both roughly equally sized in terms of GDP per capita. How this reflects differences in power is not easy to tell.

Needless is to say that the ideology of the government is also crucial in this respect. Left-wing governments are more keen on raising the minimum wage than right-wing ones.

Additionally, the structure of production and workers' skills varies among countries. This means that some countries could have a lot of workers around the minimum wage, giving more incentives to unions to fight for them, with greater opposition from business. Yet, businesses in a country with few people around the minimum wage are more willing to accept a minimum wage, because it affects less people.

Last but not least, what matters is the real minimum wage. Equally sized countries can have different price levels, because the basket of non-tradable goods might be very different (prices of tradable goods are supposed to equalise in the long run, according to the Law of One Price).


What textbook can I use to understand engineering economics?

These are the recommended books for our Engineering Economics university course:

Main Textbook

N. M. Fraser and E. M. Jewkes, Engineering Economics: Financial Decision Making for Engineers, 5th edition, Pearson, Toronto, Ontario, 2013

Supplementary Resources

D. G. Newnan, J. Whittaker, T. G. Eschenbach and J. P. Lavelle, Engineering Economic Analysis, 3rd
edition, Don Mills, Toronto, Ontario, 2014.

J. A. White, K. E. Case and D. B. Pratt, Principles of Engineering Economic Analysis, 5th edition, Hoboken,
NJ, USA, 2010.

I found the main textbook easy to follow. Hope this helps!


Which regression technique is used for calculating price elasticity in practice

They use all types of methods.

Often, papers will use a very basic linear regression model and submit to a low prestige journal so one can justify ones grant and boost the H index as much as possible when meta analyses cite your article to clean up the mess.

It's up to the meta analysis to take into account that not all models have correct methodology and create a meta-analytical model to correct for all these inaccuracies.

Fortunately, there are sometimes dynamic lag models and the like to compare to and guess at an appropriate correction term for the others.

Here is an example for gas prices.

https://www.google.com/url?sa=t&source=web&rct=j&url=http://papers.tinbergen.nl/06106.pdf&ved=0ahUKEwia2Me-85LLAhUUzmMKHWvjAd0QFgghMAE&usg=AFQjCNHi6RH_xX7h9NLPcVRCFAMP0OtERQ&sig2=3ChFSPGwukN_WC4IlhitGA

Edit: A meta analytic model is a model to explain why studies obtained the results that they did. Different samples, methodologies, and so forth will produce different results, and a metamodel expresses this formally.

A dynamic lag model (also called a distributed lag model) is a model to predict the current value of a dependent variable based on past values of independent variables. For example, if a change in price causes a change in demand, you can model this by correlating price at time t-1 to demand at time t.


Let $X$ be the original regressor matrix and $Z = [z \;\; zd]$ be the intstruments matrix, and consider the IV estimator

$$\hat \beta_{IV} = (Z'X)^{-1}Z'y = (Z'X)^{-1}Z'(X\beta + u) = \beta + (Z'X)^{-1}Z'u$$

As long as the things that are included in $Z$ are orthogonal to the error term, we get consistency... provided that $Z'X$ remains invertible, something that you can easily check in your case.


Do rich people on average have the vast majority of their wealth invested or they have lots of liquid cash/no productive properties?

The Federal Reserve has two statistical programs that I believe might address your question. The first is the Survey of Consumer Finances (SCF) and the second is the Flow of Funds (FoF). You should be able to find the data on the Fed's website.


Cobb-Douglas production function with expenditures rather than units

Sure you can, just that your interpretation of your variables in your analysis changes however. In this case you are analyzing how investment in differing factors of production affect output.

I'd recommend that you may want to estimate a more flexible functional form like the Translog Production Function to check if your function is CES instead of just a simple cobb-douglas.


Deflation and positive real interest rate

Deflation means decreasing prices, but with the rigidity of wages (although this depends on the country in which you are), it means (i) higher unemployment rates or/and (ii) lower profits.

(i) higher unemployment means less consumption and thus more deflation which drives the economy into a vicious circle.

(ii) lower profits means lower investment in production capacity or R&D, etc... and also lower dividends, which to me is more desirable than (i). Since the majority of people are less exposed to variation in capital income than in wages, but this is political.

In this framework, if the central bank plays its counter-cyclical role, key rates should decrease to discourage savings, conditionally to the fact that those are not already too close from 0.

But I would say that in reality, many other determinants must be taken into account. Actually the trajectory of your economic system with its inertia, etc... e.g.: the fact that this deflation is imported or not;  the fact that the forecasted rentability of investments in alternative production capacities are conditional to high (or at least stable) prices so as to be competitive (example of renewable energies VS a decreasing fuel price); the shareholding structure of the economy under question, etc... Thus it may not be dangerous.

You cannot figure out why the deflation is considered to be such a big dangerous for the economy with the (partial equilibrium) numerical example you give.


Can you use more than one instrumental variables in an Econometric model?

If you have endogeneity between a dependent variable and error term the use of Instrument variables are the way to go.

as long as $\mathbf{COV}(x_1,z_1)\ne0,\mathbf{COV}(x_2,z_2)\ne0,\mathbf{COV}(z_2,e)=0$ and $\mathbf{COV}(z_1,e)=0$ you can do so.


If you are only looking for references, try google scholar: https://scholar.google.be/scholar?start=0&q=economics+%22without+convexity%22&hl=nl&as_sdt=0,43.

Among the papers on the first pages of results :

Conley, John P., and Simon Wilkie. "The bargaining problem without convexity: extending the egalitarian and Kalai-Smorodinsky solutions." Economics Letters 36.4 (1991): 365-369.
Yamazaki, Akira. "An equilibrium existence theorem without convexity assumptions." Econometrica: Journal of the Econometric Society (1978): 541-555.
Hurwicz, Leonid, and Stanley Reiter. "On the boundedness of the feasible set without convexity assumptions." International Economic Review (1973): 580-586.
Hamano, Tadashi. "On the existence of equilibria without convexity or free disposal on production technologies." Journal of Mathematical Economics 23.6 (1994): 565-583.
Shapley, Lloyd S., and Martin Shubik. The core of an economy with nonconvex preferences. No. RM-3518-PR. RAND CORP SANTA MONICA CA, 1963.

Then look at the papers who cite the above papers. That usually allows you to uncover other relevant references.


The tradeoff between work and leisure has been extensively studied.
I would start with the wikipedia entries on labor supply:

  https://en.wikipedia.org/wiki/Labour_supply
  https://en.wikipedia.org/wiki/Labour_economics#Neoclassical_microeconomic_model_.E2.80.94_Supply

If you are looking for more complex articles, Heckman in particular studies this, but it is econometrically heavy for undergraduates.

Note that there are two different effects you are asking about. First, you are looking for an economic explanation or model of why people find themselves regretting the amount of work they are doing after the fact. Depending on how you define regret, this can be modeled by allowing the agents to be surprised by their own mortality or other statuses.  They will, after the fact, wish they had not valued the money so highly!

Secondly, you are looking for agents who explicitly value money tomorrow more than today. Awkwardly, the literal interpretation of this will not get the behavior you are looking for. If agents actually valued having money tomorrow more than today, they would never spend money and only save it! But, it is perfectly possible that agents value the money in the future too much, just not more than today.  If this still describes what you are looking for, you should take a look at inter-temporal choice, particularly hyperbolic discounting.

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

To help exemplify this second point, let us consider the case of normal discounting rates $\beta$, where each period is worth a fraction of the prior period, say $\beta=1/2$ the value. The value from now to forever is:

  $=1*0.5^0+1*0.5^1+1*0.5^2+....$

  $=1/(1-0.5)=2$ by infinitely repeated fractions.

But what if $\beta$ is greater than 1? Say $\beta=1.1$?

  $=1*1.1^0+1*1.1^1+1*1.1^2+...1*1.1^{999}+...$
  diverges to $\infty$ by sum of infinitely repeated values, a sign of problems to
  come.


Is elasticity of substitution between goods empirically constant?

Constant elasticity of substitution (CES) Utility functions imply demand functions that are linear in (i.e. conditional on prices they are constant fractions of) income (see Rutherford's Lecture Notes on Constant Elasticity Functions). However, there is empirical evidence for both superior goods (demand that increases faster than income) and inferior goods (goods that decrease in income), this does not actually hold for real preferences.

The fact that household budget shares on each consumption good (or even category) are not constant is probably additional evidence against CES. In principle it is explainable if different consumers face different costs for goods but shouldn't explain why some people choose to do entirely without certain categories of consumption (no TV rather than an inexpensive one). I say probably because:

If you define the category wide enough (say two, durable and non-durable consumption), everyone does likely does consume something in every category and maybe in close to fixed proportions.
For certain important forms of consumption like cars and house, adjustment costs and indivisibility may make households face very different prices.

But, as @regressforward suggests, it may still be an acceptable approximation.


Have countries with a monopoly on a resource in an area ever established 1-company monopolies in that area to drive up price?

Yes, you are describing the (British) East India Company.  A royal charter granted it a 15 year monopoly on English trade with "all countries east of the Cape of Good Hope and west of the Straits of Magellan".  Also noteworthy, "at the height of its rule in India, the British East India company had a private army of about 260,000 — twice the size of the British Army".


Calculating the cost of a year without land-tilling

The approach appears to be static. An intertemporal approach would recognize that part of the period's forgone production would have been used for investment, augmenting the capital base. So we also lose future consumption. But let's skip that.

In order to categorize what the country will lose (and who is going to lose it) I decompose output into certain categories and comment on each one

Output =
+ Imported materials  (we don't care what happens to them)
+ Services offered from abroad  (we don't care what happens to them)
+ Locally produced materials in other sectors  (will there be demand for their products from other sectors of the economy? Will they be able to export them? -possibly lost income here)
+ Services offered by locals (How much of them will be used to manage the compensating imports? -possibly lost income here)
+ Rents of land owners who do not engage in farming (lost income)
+ Wages of people working the land, i.e. "Direct Labor" (lost income)
+ Wages of other personnel in the sector (will they be needed to manage imports? -possibly lost income here)
+ Depreciation (equipment will erode by the simple passage of time, but land may even appreciate in terms of productivity-after all the biblical commandment has clear practical motives. So on the balance?)
+ Profits of farmers/entrepreneurs (lost income)

A basic conclusion from the above, is that by stopping production in one sector, we may also lose some production in other sectors.

...and think also about taxes.


Does Abatement Cost Mean Cost of Reducing Emissions with Output Held Constant?

No, there's no assumption that output is constant.

Indeed, in most cases, it's expected that output will change, if only a little.

For example, there are abatement costs that involve a one-off increase in output which brings about a long-term decrease in output, such as insulating buildings to abate greenhouse-gas emissions from fossil-fuel powered heating.

Let's take the case of an Australian dwelling being insulated late in the year, during the start of summer, in November 2014 (I'll take the accouting year to be the same as the calendar year, rolling over on 1 Jan). So for 2014, we have had full heating costs from the winter that took place May-August, as well as the manufacture and installation of the insulation materials; so 2014 sees an increase in output over 2013. But in 2015, that insulation won't be manufactured or installed, and the dwelling's heating demand will be lower, so 2015's output is below that of both 2014 and 2013.

To produce output-constant costs would require putting the abatement measures through a macroeconomic model, as part of their cost-estimation. Abatement cost calculations are messy and uncertain enough as it is (before we even get onto the problems with marginal abatement cost curves, one of their more common presentations), without adding a whole extra layer of messy uncertainty that is macroeconomic modelling.


If your paper is rigorous enough, you might want to submit it to a peer-reviewed specialized journal. Journals pertaining to Economics are the Quarterly Journal of Economics, Journal of Macroeconomics, The Economic Journal, and many others. Many of these journals have a link for writers to submit their paper and a "guide for authors". Likewise, search on Google for Call For Papers.

The question by @MichaelGreinecker (whether you know anyone in the field) makes me wonder whether being accepted for publication also depends on having connections with the editorial, rather than being exclusively a matter of rigor of the research.


What statistical techniques can be used for measuring price elasticity

There are typically two ways to go about estimating causal relationships in the data: by using exogenous variation in the "right-hand-side" variable, or by using structural estimation.

Unless there's an experiment or a quasi-experiment of some sort, the technique economist's apply is instrumental variable (IV) estimation. The idea is that to find the causal impact of x on y in a situation in which x is partly determined by y itself or by a third variable that also affects y, is to find a source of variation that only has a direct effect on x.

Machine learning techniques are not typically used to estimate causal relationships. Instead, they are often used for forecasting.

Mixing the two: machine learning and causal inference is a focus of current research, but apparently there are no solid, off-the-shelf methods to use. Intuitively, the issue is that in the example above, you will need all of x to produce the best forecast of y, but you want only the exogenous par of x to get a sense of the causal relationship between x and y.


Why two goods are of different types of Cross Elasticity if we swap them in formula?

The cross elasticity of demand $E_{XY}^D$ is defined as the percent change in quantity demanded for X divided by the percent change in price of Y, holding the price of X fixed.

The problem with your calculation is that you did not use the quantity change when the price is fixed. Therefore, you need additional data to calculate the cross elasticities.

Another part of your question is whether cross price elasticities are symmetric. The answer depends on what type of demand you are working with. For Hicksian demand (where we hold the utility constant), the cross elasticities are symmetric. For Marshallian demand (where we hold the income constant), cross price elasticities are generally different, even the signs could be different. The reason that the Marshallian demand has different cross price elasticities is that when price of a good changes, it has both price effect (self and cross) and income effect (self and cross). The "pure" cross price effect should be symmetric, which is what we see in Hicksian demand. But the "gross" cross price effect, the combined effect of price and income, is not symmetric, because the income effects caused by different goods can be very different. For more details about Hicksian and Marshallian demand, price and income effects, consult any decent intermediate micro textbook. Or probably Google will show you plenty lecture notes on these topics.


Increasing prevalence of price discrimination

For current data, you could use a scraping program to collect data from firms' websites. This was the approach taken by a recent paper for airline pricing, so it would definitely work for your first example. In general, it should work for any market that has a significant online presence with universal price quoting. Retail sales should more or less satisfy that description. Grocery stores too nowadays, at least the big ones. See the paper cited for more info on its methods.

But, as your question pertains to the evolution over time, you'd need past data as well. Have a look at The Billion Prices Project at MIT and Harvard. http://www.thebillionpricesproject.com/datasets/

Williams, K. R. (2017). Dynamic Airline Pricing and Seat Availability (Cowles Foundation Discussion Papers No. 2103). Cowles Foundation for Research in Economics, Yale University. Retrieved from https://ideas.repec.org/p/cwl/cwldpp/2103.html


I don't know why these two method yield different solutions for marginal product of labour

I am not sure why you expect method two to work, as generally
$$
\frac{\partial F(K,N)}{\partial N} \neq \frac{\partial F(K,N)}{\partial K}\frac{\partial K}{\partial N}.
$$
These are partial derivatives. Unless information to the contrary exists
$$\frac{\partial K}{\partial N} = 0.$$
You can easily see that these formulas are not connected by calculating the area of a rectangle which sides have lengths of $a,b$. Thus $A(a,b) = a\cdot b$ and
$$
\frac{\partial A(a,b)}{\partial b} = a \neq b \cdot 0 = \frac{\partial A(a,b)}{\partial a}\frac{\partial a}{\partial b}.
$$
In case you insist on homogeneity of degree one you can take the square root of the area.


Are BLS labor productivity measures in current dollars or constant dollars?

It depends on which BLS numbers you look at

For example in Table 2 of http://www.bls.gov/opub/mlr/2013/article/industry-employment-and-output-projections-to-2022.htm it gives for manufacturing output:

Billions of chained 2005 dollars            Billions of dollars
      2002        2012                        2002        2012
    4,320.8     4,407.6                     3,848.3     5,604.8

and the first pair of numbers uses a constant dollar (more precisely a chain-weighted dollar) measure while the second pair uses a current dollar measure

For general productivity measures, the BLS says for example at http://www.bls.gov/opub/hom/pdf/homch12.pdf

  Time-series indexes of manufacturing labor productivity (output per hour) and unit labor costs are constructed from three  basic  aggregate  measures:  Total  real  output,  hours worked,  and  nominal  compensation.

So output per hour aims at a real measure of productivity, while unit labor costs divide a nominal figure by a real figure to give something which combines real productivity and nominal wages


Is Debt Monetization Equivalent to transferring money from rich to poor?

In the US, broad debt monetization would produce the following losers:  owners of cash, bonds or equivalent fixed income securities- notably, China, Japan, and the other foreign US creditors, and entitlement holders such as pensioners.  The winners would be:  anyone who owns a real asset on a leveraged basis, such as property owners who have a mortgage, corporations who have borrowed a lot versus their cash flows.  This isn't really rich->poor, it's lender-> borrower.   This seems like an attractive policy for the US, although there's a very strong argument that the US dollar will permanently lose its reputation as a store of value if it were ever implemented.


Etymology and Introduction

As a concept to measure the interchangeability of assets and money, liquidity is a new word. It first appears in 1923 in a use by Hawtrey (The Oxford English Dictionary (1989)). The underlying idea however is much older. Menger (1892) calls a good more or less saleable according to the facility to which it can be disposed of at current purchasing prices with less or more diminution. He is talking about the origin of money, and not the disposition of financial assets, but this concept of saleability is very much like the modern concept of liquidity. This sense of saleability goes back at least as far as Jehan Palsgrave's usage in 1530 (The Oxford English Dictionary (1989)). Although the absence of liquidity is now commonly referred to as "illiquidity", Marschak (1938) offers "frozen" as an alternative that did not catch on.

Hicks (1962) says that the use of the word liquidity in a financial sense was popularized by Keynes and the Macmillan Report in the 1930's (Macmillan Committee of HMSO (1931)). In The General Theory, Keynes says that liquidity justifies money trading at a premium over bills or bonds and causes the existence of an interest rate. Hicks teases from Keynes' Treaty on Money (Keynes (1930)) that one calls an asset more liquid than another if it is "more certainly realizable at short notice without loss."

This quote is more ambiguous than it may seem at first glance. Hicks offers several interpretations. The first, which he flatly rejects, define liquidity as the difference between the price the owner carries on his books for an asset and the price they could sell it for on the market. The second, is an interpretation of marketability. Hicks defines a security as marketable if it is sold just as well after negotiation, search and advertising as it is without it. That is, we can compare the liquidity of two assets by the relative sacrifice one makes from a rapid sale. He claims this interpretation is "more appealing" but still not what Keynes meant. He understands Keynes's definition of liquidity to require perfect marketability, but even perfectly marketable assets can be more or less liquid. The difference here is in the moments of the asset's price. Among marketable financial assets, we can regard them as more or less liquid by using a utility function to manage the trade-offs between maximizing the desirable odd moments (e.g., positive mean and skew) and minimizing undesirable even moments (e.g., variance and leptokurtosis) of asset returns.

Definitions of Liquidity

A treatment of market liquidity is critical if we are to realistically model the behavior of markets of financial assets that are traded with transaction costs. The liquidity literature primarily follows three meanings of liquidity. The first and oldest class of measures of liquidity relate the size of loss to the amount of notice. That is, what is the fraction of the best possible price that a seller can net as a function of time allotted to conduct a loss minimizing sale? Keynes's definition is a specific example where he is interested only in short notice. For example, compare trying to sell a round lot (100 shares) of IBM stock today with doing the same with a home. The IBM shares will sell without diminution. The home will sell at an enormous discount because of product heterogeneity, heterogeneous buyers, and the skipping of time consuming risk avoidance techniques (e.g., title search and property inspection). A classic statement of this sense of liquidity comes from Hirshleifer (1968). He calls liquidity "as asset's capability over time of being realized in the form of funds available for immediate consumption or reinvestment -- proximately in the form of money." Admati and Pfleiderer (1988) also care about liquidity in this first sense. They see exogenous liquidity events (say a change in margin requirements) causing a "demand for immediacy" (a term also found in Grossman and Miller (1988)), that is, the willingness to sell rather than wait when doing so costs the seller. According to Greenbaum (1971), who called this the L1 definition of liquidity, the earliest work on this sense of liquidity was in Tobin's unpublished manuscript. Pierce (1966) elaborates this notion and explores this measure in the context of commercial bank portfolio management.

The second meaning understands liquidity as the expected time to sale without diminution. Returning again to the example of selling the lot of IBM stock and a home, the expected time to getting the best price on your shares of IBM is almost zero (at least during business hours and with access to a computer or phone) whereas, on average, the home would take a few months to sell optimally. Lippman and McCall (1986) explore this sense of liquidity. They take an agent as choosing a stopping rule $\tau^* \in T$ of all possible stopping rules to maximize expected net receipts (under that stopping rule) of $E [R(\tau^*)]$. They define liquidity as $E [\tau^*]$, the expected time to sale under the optimal stopping rule. Krainer and LeRoy (2002) also suggest this expected time to sale under an optimal selling rule a measure of liquidity.

The third definition of liquidity involves the uncertainty of an asset's value. As discussed above, Hicks (1962) sees this as the critical attribute of liquid assets. Proponents of this definition argue that it is of little importance if you can sell an asset on short notice and with small loss if the asset itself is worth little when you need it. That is reasonable as long as investors are risk averse. Tobin (1958) introduces this sense of liquidity in a framework of risk averse investors and uncertainty of future interest rates. Tobin's paper resolved the paradox (to economists) that consoles (perpetual bonds) have higher expected rates of return than cash investments. Lagos (2008) is able to explain much of the risk-free-rate and equity-premium puzzles in an economy with riskless, liquid assets and risky, illiquid ones when agents hold assets for the liquidity services.

Deaton (1991) equates a model's liquidity constraints with limitations on borrowing future income. In that sense, liquid assets are more effective in moving income though time. Holmstrom and Tirole (1998) also explore this meaning of liquidity as a way of storing wealth between periods in search of recommendations for efficient market making of financial assets and optimal provision of liquidity services by the government. Hovakimian, Opler, and Titman (2001) continue to explore this inter-temporal liquidity concept in economies with human capital that creditors cannot seize or make claims upon. He experiments with two an extra features, either no short selling of physical capital or limited borrowing by agents.

The concept of liquidity is a mixture of the attributes of liquidity discussed above. Households without liquid assets want to borrow with their illiquid assets as collateral. They cannot do so. Creditors are unwilling to lend because in the event of default, lenders will have to sell collateral with serious discount from the true value, with long waiting times for finding a good selling price, or with unfavorable co-movement (e.g., covariance or other forms of joint distribution) with other asset holdings. This idea of liquidity as having wealth when you need it is similar to the CAPM insight of weighting future returns by the marginal utility. Krainer and LeRoy (2002) argue that the CAPM holds for illiquid assets as well as liquid ones, but that observing the relevant shadow prices is difficult. However, the ambiguity of asset values in the presence of illiquidity (explored later) ensures that these shadow prices will not be unique. The general idea is that illiquidity can drive a a wedge between the discounted expected risk adjusted returns and the price for which you can sell an asset. As such, the traditional CAPM model is not entirely appropriate. An area for future investigation is expanding the CAPM to make this distinction. Holmstrom and Tirole (2001) was one attempt to so, but focused on firm decisions and not investor valuations.

Can the Alternative Definitions of Liquidity be Reconciled?

Here is an example of the wedge between expected returns and price that also highlights that an asset can be liquid in one sense and yet is illiquid in another. Imagine an investor with a US Treasury bond with a 14% coupon in a market where newly issued bonds have a coupon of 7%. Selling these highly appreciated bonds (which traded far above par) would require paying significant capital gains taxes, while holding them involves minimal credit risk and the interest would be state and local tax exempt.  Therefore, to sell would be expensive for the owner and therefore illiquid in sense 1. Nevertheless, these securities could surely be sold rapidly though the secondary US Treasury Bond market and there would be little benefit to waiting and therefore highly liquid in sense 2. It is certainly the case that assets that are liquid in one sense are often liquid in the others too. But as this example shows, that need not be so.

Therefore we cannot collapse these three definitions into a single numerical measure of liquidity capturing all the desired attributes. Even if we could, Hicks (1962) and Admati and Pfleiderer (1988) have concluded that liquidity is an ordinal property. Marschak (1938) suggests measuring the distinct properties of liquidity separately. Pierce (1966) disagrees that such an ordering is possible, at least with respect to sense 1 of liquidity. He says that "apart from those assets that are perfectly liquid and those for which sale prior to maturity is impossible, assets cannot be uniquely ranked by degrees of liquidity." His example is an asset A that is easy to  sell well with short searches (but no better after long ones) and an asset B that sells more poorly than A for short searches and better than A for long ones. Pierce also notes (in describing the weakness of his notion of liquidity) that "the price per unit often depends on the number of units sold." This allows for another example of the crossing behavior, where one asset may be more liquid at small quantities but less liquid for large ones.

Admati and Pfleiderer (1988) argue that liquidity of the third type actually encourages liquidity of the first type. They argue that commodities with successful futures markets have demand for immediacy because price volatility and risks of delaying sales are large (illiquid in sense 3). Such markets also help spread the fixed costs of market making (waiting around for buyers and sellers to want to trade, as well as infrastructure) across a large number of market participants.  In contrast, home sellers are less concerned
with short-term price volatility and instead prefer an extended search for potential buyers.

Alternative Definitions

Krainer and LeRoy (2002) argue that liquidity is a feature of markets and not of assets. They offer as a first example that a Ford automotive factory is illiquid but Ford stock is liquid. A second example is a pool of mortgages which is more illiquid than than the underlying mortgages. However, perhaps it suffices to refer to an asset's liquidity as its liquidity in the most liquid market an agent can trade it in. Tobin calls reversibility "the value of the asset to its holder expressed as a percentage of is contemporaneous cost to the buyer" (Tobin and Golub (1998)). Hahn (1990) sees asset liquidity as being closely related "to the cost reversing a decision taken earlier." His example is that for economic agents, the cost of selling an asset in period 2 will factor into the decision to invest in it in period 1. This meaning of reversibility is a mixture of sense 1 and sense 2 of liquidity. If waiting for suitable trading opportunities is the expensive part of reversing a trade then reversibility is primarily the second sense of liquidity. However, if transaction costs are the expensive part of reversal then the first sense of liquidity will be the relevant one. Marschak (1938) develops a concept of plasticity that is essentially this concept of reversibility. He considers plasticity as a
more general concept than saleability that includes flexibility. Greenbaum (1971) is an early reference that notes the interrelatedness of reversibility and liquidity.

Jones and Ostroy (1984) see an essential attribute of liquidity in the related measure of flexibility. Financial investments that leave agents with a larger set of intermediate and final choices are more flexible. Lippman and McCall (1986) are similarly interested in liquidity as flexibility. They show in a simple search model that if the investor opportunity set changes over time, even risk neutral investors will demand a more
liquid asset because it improves the expected returns of their portfolio. Hahn (1990) also sees liquidity as tied to the "speed of response to new information," but it is hard to see if this is a cause of liquidity or caused by it. Intuition suggests the former. Few market participants, high transaction costs and other direct causes of illiquid markets all make it more difficult to trade on new information. Further, the trees model
of Lucas (1978) shows that prices can move even in the absence of trade. Frequent trade is not synonymous with liquidity.

The frequency of order arrival is a determining factor of liquidity in markets with market makers. This implies that markets need not be liquid to have fast adjusting prices. Flexibility can be about more than investment opportunities. If agents are borrowing constrained and goods can be purchased only with liquid assets, then agents must hold some liquid assets to purchase goods for immediate consumption, even though doing so does not maximize their portfolio return. If consumption opportunities vary over time then agents will hold relatively more ready assets to take advantage of fleeting consumption opportunities. If agents were not so constrained, then they could simply borrow against their illiquid assets (repaying debts as the returns arrived or when prudent sale was possible) to consume as they like. This is also true for investment flexibility. If investors could borrow against their illiquid assets then they would not need liquid holdings to take advantage of future investment opportunities. The necessity of holding liquid assets for consumption purposes is one motivation for the money-in-the-
utility function (MIU) literature. The MIU framework puts money, an asset without consumption value nor any investment return, into the utility function as a reduced form representation of all the ways that money (as the most liquid asset) makes consumption easier and more efficient.


Democratic elections of board members by employees?

In Sweden, 2 employees are members of the board in companies over 25 employees (by law). So at least here, not all board members are choosen by the shareholders.

One of the reasons many shareholders have stocks, is to influence the long term way the company is run. The main way to do this is by influencing which people are on the board. If big shareholders could not influence this, the cost of new capital into the companies would increase very much. Because with less influence there is a larger risk, and the way you pay for larger risk is by larger return on investments.

So in short, while it seems on the surface that it could limit the "short-termism", as you call it, it may actually have the opposite effect.


When a country loses access to markets they mean access to debt markets. Here is a quote from a paper if you need a citation:

  Greece lost access to credit markets when debt exceeded 130% of GDP,
  whereas Belgium successfully handled identical debt relative to GDP.
  What determines the highest debt that a country could repay, defined
  as “fiscal limit?” Can we estimate the fiscal limit for a country
  which has never lost access to markets? We use historical data on
  primary surpluses (surpluses excluding interest payments) and debt
  relative to GDP for six high-debt, developed countries, two of which
  lost access to markets after the 2008 global financial crisis, to
  estimate the behavior of the primary surplus in response to changes in
  debt. The high-debt countries include Belgium, Canada, France, Greece,
  Italy, and Portugal.

Predicting Sovereign Fiscal Crises: High-Debt Developed Countries (Daniel and Shiamptanis (2015))

Specifically, "closed" means that that the sovereign can no longer raise funds from capital markets. Depending on the specific situation, some entities within that country can continue to raise external funds. A business with significant assets or good prospects may be able to continue to raise funds even when the country cannot. In Greece, at least until recently, this was the case. See the article Greek companies get back on the road, with help from bonds.


How wealthy are modern day humans compared to preindustrial times?

There is significant debate about whether such x exists.

For example Louis XIV had things in his estate that are not in mine (I do not have two palaces). But there are also things in my estate that were not in his. I have access to 21st century healthcare, dental care, the internet, I can flush after going to the toilet...whose lifestyle is 'worth' more?

The comparison gets more difficult the farther you go back. So hunter gatherers, with tribal ownership, without any currency and frequently without any sort of industry are probably uncomparable to modern humans via any 'wealth' based standard.