Does it affect locals negatively when a new company moves in? I want to specifically discuss Dallas,TX if possible but a broader answer is still appreciated.

Ever since Toyota and State Farm moved to Dallas, among others, the housing prices have sky rocketed to unreal proportions. Rent prices are at least $200 more than 2 years ago. Houses are about 40% more expensive from 5 years ago.

They say that this is great economic growth for DFW but is it really? How do the locals adapt to this economy? No longer are they building houses in the 200s or 100s. Anything in the 100s is 30 years old and minimally updated.

Are the incoming employees keeping their California wages, causing this inflation? I know for a fact those who sold their homes come in overbidding for much more because in their perspective the houses are cheap.

But what is going to happen to the locals? Will the companies have to raise wages to keep market? Or are local people buying homes on salaries that cannot afford it and wages not go up and cause a crash?

What usually happens in this scenario?

This is a very unique situation in Dallas/North Texas. This is the absolute worst economic growth possible for locals. Here are two key elements to look into:

Wages are fairly stagnant in DFW. This paired with 40-50% home/rent increases is the first warning sign.
Re-assessed tax values - expect numerous foreclosures in the next two years. Many people are purchasing homes without even thinking about the fact that many cities in North Texas have property tax rates of nearly 3%. While mortgage companies are quoting "monthly payments" based on the previous year's taxes paid on those properties (many of which are capped by homestead exemptions and have been for decades), once taxes are reassessed the following year, their payments will spike significantly. Here is an actual example from Sachse, Tx :

Total monthly payments on a 240k home for the last 6 months of 2016 were 1,536. The family accounted for this in whether or not they could afford this home. Their payment after the new tax appraisal is 1,860 per month. That is a $330 increase that they were unaware of and can no longer afford, as they purchased on the upper end of their budget. Expect to see this across the board, even as those from locations such as California will be paying more than the California state average for monthly house payments.

The average monthly payment in California was 2,542 on a 480,000 mortgage. Their annual property tax on average was 2,500. A 480,000 mortgage in Sachse, Tx with 10% down would currently run you about 3,640 per month with an annual property tax of 14,000. (Sheds a little light on the 'but we don't have a state income tax' argument, and how invalid it truly is. They pay the state income tax in the form of double/triple many other states' property tax.)

Beyond this, North Texas has room to expand, significantly. While the heart of Dallas may reach its limit and increase in price exponentially, the suburbs will likely take a large hit in value once construction begins to catch up. The home values are already overheated, and this is, by definition, a housing bubble.

My best guess for when it will pop is around 2018-2019 when we begin to see delinquent payments. Anyone purchasing a home between 2016-2017 in North Texas may be upside down for 5-10 years.


Is it possible to commodify the State? Karl Marx said that capitalism commodifies everything. So I'm wondering if that rule applies to the very State itself.

Yes. For example, the nationalisation of industries brought expanded reproduction into the state function. So do public private partnerships. The generalisation of private sector management standards and language have made obvious the service provisions of commodities to customers in areas like welfare, defence procurement, archival or parliamentary services.

I commend Volume 1 of Capital to you, in particular chapters 1 to 6, because your use of the term in commentary differs significantly from that present in the literature you reference.  The value-form, as in "the commodity," is a social relationship not an object.


What are the economic drivers of urbanisation? It is regularly argued that urbanisation drives economic growth (e.g. here).
I wonder which are the drivers that lead to urbanisation in the first place?
I believe the historical case of the industrial revolution and the contemporary case of urbanisation in less-developed countries are relatively more straightforward (impaired subsistence farming vs participation in the global economy). But I wonder why there is also increasing urbanisation in developed countries (e.g. Schwarmstädte in Germany), in which there is economic opportunity outside major cities as well.
Are there concrete economic dynamics that explain why people continue to move to cities even in industrialised countries? Or do cultural or lifestyle factors predominate?

This is quite broad question, in fact the full answer to your question would probably require a textbook on urban economics, so also this answer is based on one; Brueckner (2011) Lectures on Urban Economics, MIT press,  which is whole book largely dedicated to this (and other urban economics) issue(s). I will try to provide summary of main arguments found in the source cited above and also some additional ones.  According to economic theory there are several reasons for urban areas cities to develop

Specialization/division of labor - the more people live in certain area the greater scope there is for people to specialize in tasks and divide labor. This in one aspect self-explanatory since clearly when you have more people you can subdivide their tasks more but there.
In turn division of labor is extremely economically beneficial and allows us to increase production and consequently our material standards of living (as already observed by Adam Smith in the Wealth Of Nations). This drives people to aggregate in cities since thanks to specialization they can enjoy greater standards of living.

Economies of scale: Some production processes exhibit economies of scale - meaning the more you produce the more efficiently you can produce. This is technically connected to the division of labor mentioned above (Brueckner puts division of labor and economics of scale under one sub-chapter), but the division of labor is not exclusive cause of economics of scale and it also enhances production on its own so I thought they both deserve their own point.

Pecuniary agglomeration economies:  "Pecuniary agglomeration economies lead to a reduction in the cost of a firm ' s inputs without affecting the productivity of the inputs" (Brueckner 2011). This is best explained by an example. Let us assume that firm needs to hire person with some rare combination of skills and let us assume only $0.5\%$ of population has such skill. Well then in a village of 100 people you would on average expect less than one such person to exist, in town of 10,000 people you would expect 50 such people, in city with 10,000,000 you would expect 50000 of such people. Hence, it would be easier to gather the special workers you need in large city than search across many small villages.
In addition when it comes to business to business (b2b) there will be higher competition among suppliers in big cities rather than small villages. Erosion of market power of suppliers reduces costs of businesses further down supply chain.  There are other examples of these effects in the above mentioned book.

Technological agglomeration economies: "Technological agglomeration economies arise when a firm’s inputs are
more productive if it locates in a big city, amid a large concentration of
employment, than if it locates in a small city" (Brueckner 2011). This is because for example, in R&D there might be technological spillovers between firms.

Transport Costs and Firm Location: Transportation costs are obviously lower in places with high population density then when population is spread very thinly. It is also more efficient for similar firm to cluster together (e.g. silicon valley, Detroit's now infamous car manufacturing).

Retail Agglomeration: Cities historically originated around some important market and generally you will even nowadays find big shopping  centers in cities not small villages. This reduces consumer costs and also their prices as there is more competition/they can do comparison shopping etc.

Geography: This one is not given significant treatment in Brueckner, but some sources consider it important (Fujita, 2002; Krugman, 1991).  Earth surface is not homogenous. Some places might have excellent harbor, be at an important trade route and so on.

There might be also some other reason in the literature, my specialization is not Urban Economics so I do not purport to know the whole corpus of this body of research, but these seem to be the major reasons given by urban economics that are widely accepted.


When calculating GDP using the income approach how do we make sure that we count only wealth produced in given year? As far as I understand, when we calculate GDP we want to estimate wealth produced during given year in given country. But it seems like it will be problematic when using the income approach (the expenditures approach doesn't suffer from this problem. After all, in the vast majority of cases people pay full price for services and goods at one sitting. When they have problem with paying full price they take a loan and then pay full price with money that they got from a creditor).

1.We include income from receiving interest because giving loans is a service. But if lending money is a service, then it can viewed as kind of service what you can spend literally years to pay for.
There will be incomes from some loans that were given in previous years, yet they they will by included in GDP of this year.

2.We include deprecation. It seems to make sense at first. After all, if an asset was produced this year and was completely worn out the same year, then it's still production. By including deprecation we avoid situation when worn out asset is treated like it has never been produced in the first place. BUT, assets that were created in previous year are also included in this calculation. Why would we include cost of deprecation of something that was NOT produced this year?

Or maybe I don't understand something and there are good reasons to make exceptions for interest and deprecation?

P.S. Okay, so we add deprecation in order to ignore it altogether, thus we don't really count deprecation of old assets. But how about income from loans given years ago?

So, if I correctly understand what I learned in comments, this question is resolved by two things:

1.We add deprecation in order to ignore it, rather than take it into account. It makes sense because incomes are already decreased by amount of deprecation, so in order to "unaccount" deprecation we must add deprecation. And due to us ignoring deprecation it doesn't make any sense to make distinction "deprecation of new assets VS deprecation of old assets", we ignore both of them.

2.Like renting, money lending is an ongoing service. You can keep extra money for some time, but with each year/month the amount of extra money that you're allowed to keep will become less and less.


Marshallian Demand for Cobb-Douglas When trying maximize the utility having a cobb-douglas utility function $u=x_1^ax_2^b$, with $a+b = 1$, I found the following formulas (Wikipedia: Marshallian Demand):

$x_1 = \frac{am}{p_1}\\
x_2 = \frac{bm}{p_2}$

In one of my books I also find these formulas for the same purpose:

$x_1 = \frac{a}{a+b}\frac{m}{p_1} \\
x_2= \frac{b}{a+b}\frac{m}{p_2}$

With $p_i$: prices of the goods; $m$: budget

I tested all of them and they produced the same results.
So are there any differences?

Since $a + b=1$ the equations are exactly the same. Substituting in for $a+b$ with $1$ in the third and fourth equations gives the first and second equations.


Gold charts on different currencies I have this two charts of gold prices for the last 60 days. One is in Dollars and the other in Euros. Why they are not an exact match?

Because they are different markets, which trade at different hours, leading to different results. There is also the effect of variations of variations in the USD/EUR rates.


Is this equivalent to the game of chicken? In the beginning of the mahjong anime/manga Akagi, the titular character has just come in from what is called a game of chicken, which is described as follows:

  Two cars head for a cliff at full speed. It's a game of reckless disregard for life where the person who first brakes loses.

No conditions for ties were given in the anime, but I will assume that:

Driving over the cliff is the least preferable outcome regardless of what happens to the other person
Losing is preferable to driving over the cliff
Being in a tie is preferred over losing, and winning is the most preferred outcome

I believe that the games aren't equivalent:

Unlike the description of the game of chicken given on Wikipedia, there is a weakly dominant strategy in the game in Akagi (i.e. braking at the edge of the cliff). But I'm not sure if the existence of weakly dominant strategies are preserved under isomorphisms of games.
The Akagi version of chicken has a pure Nash equilibrium where both players take the same action (and both obtain an outcome that isn't their most preferred outcome). This outcome (in terms of preference rankings) doesn't exist in the standard formulation of the game of chicken. However, thinking about it, it might be better to view the Akagi case as something continuous that might then permit mixed strategies (with regards to when the player chooses to brake).

Is my reasoning here correct, or is there a better way to view this?

Update: I should add that in the Akagi version, the choice is when to brake, which lies on a continuum in some interval of $\mathbb{R}$. (I'm not entirely sure how to formulate this mathematically, since we also need to allow for the possibility that the player drives off the cliff, but this is beside the point.) However, as previously stated, braking right at the edge should be weakly dominant, at least from an intuitive perspective.

Conversely, in the standard version, there is a binary choice ("swerve" or "straight") at a single point in time. Unless it's possible to rule out all but a few cases in the Akagi version, it would seem that the games can't possibly be equivalent, since we can't "reduce" the Akagi case to the normal game of chicken. (There should be no isomorphism between them if the cardinalities of the set of choices are different.) But that's exactly the thing that I'm not completely sure about.

Moreover, I'm not sure if I'm analysing the equilibria of the Akagi case properly, particularly since it strikes me as a scenario where mixing might do better (but I'm not familiar with mixed strategies in continuous cases, and I'm too busy at the moment to test somewhat "simpler" cases).

As you stated the strategy set in the traditional chicken game has two elements and in the Akagi version it is an interval, so there will be no bijective mapping between the two.
If I misunderstood and you are using a different notion of equivalence please clarify.

Also as you state in the Akagi version there are weakly dominant strategies but in the traditional version there are none. I believe the story of the traditional version is rather about two cars driving towards each other and you have to decide whether to swerve at the last moment or trust that the other driver will. Even if you were to extend the strategies to allow swerving before the last moment this game would differ from the Akagi version, because here not swerving at all is better than swerving at the last moment if the other driver swerves at last moment whereas swerving at the last moment is better if the other does not swerve at all, hence there are no weakly dominant strategies.


What is the name and the mathematical model of this effect Consider the case when some economic variable affects some another (for example, interest rate affects price index) because it involves human decisions. The effect is not immediate and would take some time to begin. I am searching for the name of this delayed effect and the formula or mathematical or statistical model behind.

I am not economist, I just thought I would find an answer in economics realm

I know that this is called price “stickiness”. Price “flexibility” is when something such as interest rates would bring prices to equilibrium quickly. Price stickiness in this case could have a delayed effect as you mentioned. We could also call this “elasticity”. For simple models rise over run can measure price elasticity. For example, a 5% change in interest rates, and it’s corresponding impact on prices could be measured by slope (rise/run).


Is there any goal of Economics? As I understand every subject has some goals for example in physics one particular goal is to have a unified field theory. Is there any one or more goals of economics. I was expecting the goals should be like 1) Define laws so that basic human needs are fulfiled. 2) Find out laws so that inequality does not increase only. 3) Define laws such that worth of the world remain in a balance this means does not increase only or decrease only, kind of bounded.
Or is there any other goal of economics?

Economics is a science and hence the objective of economics is the same as of any science just applied to the specific discipline. Thus this objective depends on how you want to define what an objective of science is and on the demarcation of what economics is.
On the objective of science:
There are many different approaches to the philosophy  of science and some of them differ in their view of what the objective of science is. Unfortunately, SE format is not suited to broad answers so it is beyond the scope of this answer to provide an overview of all possible philosophical views on what the objective/purpose of science is (although I encourage you to have a look at some textbook on the philosophy of science such as Rosenberg, Philosophy of Science: A Contemporary Introduction; Okasha Philosophy of Science etc.)  but it is generally agreed by the philosophers of science that the main purpose of science is to explain or understand and predict various phenomena (see Purtill 1970).
Under this predominant view, in the same way, as it is the objective of physics to explain gravity, not to use it to improve people's lives, it is also objective of economics to explain various economic phenomena (trade patterns let's say) not necessary to help anyone by explaining them (however, people are not just scientists they are also moral beings, and as such many scientists might be motivated by helping others, but these are personal goals not goals of the science itself, and also as mentioned above there might be philosophers of science that would disagree with this view - but this view is quite general).
Demarcation of economics:
Currently the mainstream definition of what economics is the one that was provided by Robbins (1935),

the science which studies human behaviour as a relationship between ends and scarce means which have alternative uses.

Again, this is not the only possible way of defining the field of economics but it is the mainstream one that you would find in almost any undergraduate general economics textbook (for further discussion on this issue consult some philosophy of economics textbooks such as Ross, Philosophy of Economics or Hausman The Philosophy of Economics: An Anthology).
Conclusion:
Consequently combining the mainstream view of what science and economics is the goal of economics is to explain/understand and predict economic phenomena.  Where economic phenomena should be understood as phenomena pertaining to human behavior in situation when humans have some ends and scarce means with alternative uses to pursue those ends.
Hence, in reference to the topics you mentioned above, the goal of economics would not be to

define laws so that basic human needs are fulfilled and people do not die of hunger due to lack of money

but to study how people fulfill their needs, what choices they make, how they can best optimize their resources in various situations - but it is not the role of economics to define laws that ensure human needs are satisfied. 'Law' in economics does not even have necessarily the same meaning as in common speech, rather in economics we try to formulate scientific laws.  You can use the knowledge of economics to produce famine as well as to help reduce impact of one.
Similarly, it is not the role/purpose of economics to:

Find out laws so that inequality does not increase only

but rather to study what drives inequality, how different policies can affect inequality etc. and then it is up to policymakers to decide if inequality is a problem and how to solve it.


When and to what extent should the money supply of a pegged currency be included in the "parent" currency? For example, the Manx pound is in a de facto currency union with GBP.  The Isle of Man Bank chooses to back IMP one-to-one with Bank of England notes, but is not legally required to do so.  At one-to-one this would automatically be part of the GBP money supply.  In principle it could expand the IMP money supply, and since IMP is tied to GBP, one could argue this increase should be included in the GBP money supply.

The Singapore dollar and Brunei Dollar are pegged to each other and the total money supply managed by the MAS.

On the other hand, the HKD is pegged to the USD without one-to-one backing and the federal reserve has no control over it (HKD being managed by Hong Kong Monetary Authority).

During the Irish Pound-Sterling pegging era, we again had separate monetary authorities, but currency union.  First through Bretton-Woods and later "managed".  In this case GBP was mostly accepted for trade in Ireland, while IEP was rarely accepted in the UK.  Would GBP be in the IEP money supply, but the IEP not in the GBP?

When performing an analysis on currencies of this nature, how should we determine what to include and not in the money supply calculation?

During the Irish Pound-Sterling pegging era, we again had separate monetary authorities, but currency union. (...) In this case GBP was mostly accepted for trade in Ireland, while IEP was rarely accepted in the UK. Would GBP be in the IEP money supply, but the IEP not in the GBP?

Let the states and the institutions declare what they want. To study economic phenomena we must look at what economies actually do. Think of something totally unrelated: each state has a specific tax structure, a range of various taxes, progressive, proportional, whatever. The official structure matters, because it anchors the effective taxation, but effective taxation itself matters more.
In your case, the Irish Economy incorporated in its workings the official monetary union, while the UK Economy didn't.

So one has to go per case, which creates a testable hypothesis:

a) postulate an hypothesis based on "casual" (i.e. true but unquantified) observations like the 2nd sentence in the above quote
b) derive the theoretical implications of the hypothesis
c) send your theory and your theoretical results out to meet the data.

For your example, assume that UK increased steadily its money supply to accommodate a growing economy, while the Irish economy was stable (or stagnant, depending on your philosophical point of view), or grew at a lower rate. If we add the GBP quantity in the money supply variable of the Irish Economy as equivalent, $1:1$ to the IEP, shouldn't we observe excessive inflation? We should. Did we?  Let's say "partly". Then we can estimate the effective ratio between the two currencies as regards their effect on the Irish Economy.


Subjective theory of value applied to gross domestic product I am trying to understand the subjective theory of value and run into some considerations that make me think I got it wrong.

As far as I understand, the subjective theory of value states that the values (prices) of commodities are not related to any objective quantity but rather are a result of individual, subjective judgements. E.g. if I decide to pay 1000 \$ for a good, then for me that good has the (subjective) value of 1000 \$, and that is all there is to it.

As a result, the gross domestic product of a nation is the sum of all these subjective judgements about the values of goods that are bought and consumed by people over a period of time.

But, as far as I know, the GDP is used to evaluate the performance of an economy (growth and so on): Do economists really base their evaluation of an economy on some non-objective measure of value? Or value is subjective at the level of an individual and becomes something objective in the aggregate?

Can someone help me out?

Interesting observation, but I think the answer is partly explained by several factors:

First, any calculation of real GDP would eliminate the year-over-year variance that could be due to taste shocks leading to significant price changes.

Second, I think your observation is partially true, however. What makes an iPhone more valuable than an old Motorola smartphone? Sure, some inputs might be slightly different, but the "production technology" of the iPhone makes it such that we (in general) do value it higher. While some measures like CPI try to account for some of those issues by considering benchmarks for certain performance criteria (like processing power, memory, etc.), in the end, there absolutely is some taste-based component.

I think it's partly accounted for by aggregation, as you mentioned, but I think the third point is that there's a bit of a disconnect between what value is trying to measure, and what GDP is interested in. Theories of value attempt to explain why we attach certain "utility amounts" to different goods. But that doesn't mean the output of any particular "theory of value" is subjective: the observed prices and quantities are quite objective. GDP (like other macroeconomic indicators) is more interested in tabulating those objective outputs of the theory of value and observing their change over time.

For example, I know I value the green mechanical pencil in front of me at \$2, despite the fact I only paid \$1 for it. Assuming the production costs are negligible, GDP would observe that as \$1 of economic activity. It doesn't consider how much I would have paid for it, which is what different theories of value attempt to explain. Furthermore, why I value it at \$2 might be for any host of reasons- perhaps its a subjective value, or perhaps that valuation reflects what I think the pencil will be worth in a year's time. Regardless of what that reasoning is, the objective measurements that GDP considers are the price paid and quantity.


Intuition for why $EV>CV$ for a normal good I understand the mathematical proof and the graphical illustration behind this property ($EV>CV$ for the variation in the price of a normal good), but I still do not understand the economic intuition. Can anyone explain, in intuitive terms, why the comparison between $EV$ and $CV$ is given by the sign of the wealth effects?

Edit: there seems to be different definitions of $EV$ and $CV$. Here are the ones that I use. Following a change in price from $p^{0}$ to $p^{1}$, the income being fixed at $w$, $EV$ is defined by
\begin{equation*}
v(p^{0},w+EV) = v(p^{1},w)
\end{equation*}
whereas $CV$ is defined by
\begin{equation*}
v(p^{0},w) = v(p^{1},w-CV).
\end{equation*}
For these definitions, if I am not mistaken, if the good is normal, then $EV>CV>0$ for a price decrease and $0>EV>CV$ for a price increase.

I think the magnitude of EV is less than the magnitude of CV for a normal good which increases in price (and the reverse for a normal good which reduces in price, so there may be a sign issue)

Intuitively, you should be able to argue that for a small price increase for an income-inelastic good (so neither normal nor inferior) the increase in income based on the new price you would need to take you back to your old utility (CV: the Compensating Variation) should be equal to the amount based on the old price you would be prepared to pay to avoid the price change (EV: the Equivalent Variation). The shift along the utility curves caused by the price change is similar in both cases as the unchanged-priced goods substitute for the increased-price good.  Graphically the two utility curves are in a sense almost equally spaced with a vertical translation

Meanwhile with a normal good, you now face a increased price for this good, so in the CV calculation not only do you need an increased income to compensate for the loss of utility, but it has to be even higher because you will want more of this increased-price normal good when you get your compensation than you would if it were income-inelastic, so CV is even higher for a normal good than for an income-inelastic good for a given level of EV calculated on the old prices.  For a decrease in prices you would get the opposite effect

Some Oxford lecture notes (pages 5 and 6) use a similar argument:

  For a normal good, for an increase in the price of good $x$, the compensating variation must be greater than or equal to the equivalent variation because once $p_x$ has increased, with $p_y$ remaining constant at $1$, it must cost more to compensate the consumer in get them back to their original indifference curve than would be required to take from them at the original price level to take them from the old to the new indifference curve, because both goods are at least or more expensive than before (another way of saying this is that the marginal utility that the consumer gets from each additional unit of cash income must be lower at the new price level, so more cash income must be given to get the consumer back to their original utility level). For a decrease in the price of a normal good, this result is reversed.


Is skills or abilities of the worker taking in consideration in Marx law of value? Is skills or abilities of the worker taking in consideration in Marx law of value?

I know about the subjective theory of value, but I want to know how Marx takes this in consideration

As far as I have been reading the law of value only takes the labour hours work to produce a commodity.

  the more labour it costs to make a product, the more it is worth, and inversely the less labour it costs to make a product, the less it is worth

let's say

product A

takes 10 hours
you need specially skills that are a hard to learn
only 14% of population already have those skills to build that product

product B

takes 10 hours
you do not need special skills
90% of the people already have those skills to build that product

A value = B value ?
I'm not taking about money price

In a real example

A web developer takes 10 hours to make a fancy custom bio web page.
A woodworker takes 10 hours to make a chair.

the chair value = the web site value?

Is the worker him or her self taking in consideration at all?

since worker1 might build a much better web site in 10 hours than worker2

Labour power is a commodity like any other. Its value is consequently a function of the labour embodied in it.

Taking your example,

product A (a webpage)
takes 10 hours
you need special skills (those of a webdesigner) that are hard to learn
only 14% of population already have those skills to build that
product

Versus

product B (a chair)
takes 10 hours
you do not need special skills (you do; those of carpenter. But those are easier to learn, compared to those of a webdesigner)
90% of the people (probably an exageration, but certainly more people have the skills of a carpenter than those of a webdesigner) already have those skills to build that product

A value = B value ?
the chair value = the web site value?

No.

While each of them requires 10 hours of labour, the labour embodied in each has a different value.

The labour of the webdesigner is more valuable.

Why?

Because it takes - say - 1,000 hours of labour to turn an unskilled labourer into a webdesigner, while it takes - say - 200 hours of labour to turn an unskilled labourer into a carpenter.


What is the percentage of free riders in public goods games? Of all experimental public good games that have been conducted in laboratory & field settings, what is the average percentage of subjects that can be classified as free-riders (as opposed to (strong) cooperators & conditional cooperators)?

So you are looking for a meta paper that aggregates results from other papers. What you mean by "all experimental public good games" is unclear because the setup of most public good games is different. The multiplicator of the public good, the number of rounds, the level of allowed commucination, whether there is punishment, etc., these frequently vary and have a very big impact on the course of play.

Another reason why such a meta paper is unlikely to exist is because the ratio of cooperators is context dependent. How the possible actions are labeled: 'Cooperation' and 'Defection' or 'Give money' and 'Save money' has a strong effect on behavior. So even seemingly similar, 'basic' games could be very different.

Your best shot may be trying to find a famous experiment and seeing if there have been any replications. You could then aggregate the data and calculate the desired ratio.


How will the Brexit affect the Euro and the Pound? The question is the title: How will the brexit affect the euro and the pound?

It looks like GBP is facing a tough situation. It is at its lowest value since 1985. Has GBP dropped permanently(or for a really long time)?

But, what will this mean for the EUR? IMO the EUR will also fall heavily, because a big country from the EU has called quits. This can potentially destroy needy countries(read: Greece).

What do you think about it? Will the Pound and/or the Euro rise or fall(compared to the USD)?

It depends. (As all things do.) I'm personally of the opinion that this will not result in any major long-run changes in the value of the Euro. This will, however, negatively affect the pound.

Most of the currency volatility that we're currently seeing is a result from investor panic rather than actual changes in economics. The UK and EU still need to discuss exactly how the UK will leave before investors can accurately assess the economic impact.

Brexit will negatively impact the EU by:

Decreasing the amount of trade between the EU and UK, due to
    increased trade barriers (this is going to affect the UK more than
    EU, mostly due to size)
Increased instability from political changes; may result in a domino effect on other troubled Eurozone countries attempting to
    leave (see: Greece)
Lack of job opportunities; the UK has a lot of foreign workers who will eventually have to go back to Europe when their
visas
    expire

Brexit will positively impact the EU by:

Forcing a possible move of the financial industry to European
centers rather than London

In my opinion, this currency change has more to do with a shift in politics than economics. The UK and EU will probably agree to some form of trade policy that will mitigate most of the economic damage.


Decreasing labor force and GDP I'm a bachelor student writing a paper on the effects of China's one child policy on future GDP. My professor told me to use growth models. However, I'm unsure which growth model I need to use for this kind of research. I'm only familiar with the Solow model and two-sector models that expands on the Solow model by introducing an R&D sector (I've seen these in their most basic form in one of my classes).

Since a major effect of the policy is a decreasing workforce, I think that technological progress (knowledge accumulation) and population growth are important, but a lot of models seem to hold population growth constant?

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


What does it mean when a bank is "fully loaned up"? Please briefly explain the situation when a bank is "fully loaned up".

The reserve requirement on deposits ensures that (for a given $reserve\ requirement(\%)$ and $deposits(\$)$): $$loans(\$) \leq deposits(\$)  \cdot (1 - reserve\ requirement(\%))$$
Fully loaned up means that this holds with equality:
$$ loans(\$) =  deposits(\$)  \cdot (1 - reserve\ requirement(\%)) $$


If women are paid less for the same work, why don't employers hire just women? If women are paid less for the same work, why don't employers hire just women? Do employers decide to lose profits just out of sexism, in feminists' and researchers' opinion? It doesn't make sense to me. In the free and competitive market, if people earn less, it means they generate, or are expected to generate, less added value (for example, because they are less educated). Am I wrong?

There could be several reasons here are just few:

Principal-agent problems. Firms are typically not managed by their owners but by managers (agents) who act on the behalf of owners/shareholders (principals). While, owners might desire to maximize profits agents can to some degree act to pursue their own goals (see discussion in Hendrikse Economics and Management of Organizations).
For example, CEO might spend more lavishly on things like their private jet, limo, having HQ built by famous architect than profit maximizing CEO would. In the same way CEO/manager with taste for discrimination might pursue that taste even at the expense of profit (to a degree of course).

Consumers could have taste for discrimination. This is in fact classic example coming from one of the earliest work on discrimination. If consumers simply have preference for seeing men instead of women at some work and are willing to pay for satisfying this preference, there will be wage gap between men and women (see Mankiw Principles of Economics pp 395).
Mankiw has a good example of this for the race wage gap, but the logic of the example directly extends to gender wage gap:

Studies of sports teams suggest that racial discrimination has, in fact, been common and that much of the blame lies with customers. One study, published in the
Journal of Labor Economics in 1988, examined the salaries of basketball players and
found that black players earned 20 percent less than white players of comparable
ability. The study also found that attendance at basketball games was larger for
teams with a greater proportion of white players. One interpretation of these facts
is that, at least at the time of the study, customer discrimination made black players
less profitable than white players for team owners. In the presence of such customer discrimination, a discriminatory wage gap can persist, even if team owners
care only about profit.

It could be result of government intervention (See discussion in Mankiw Principles of Economics pp 395).
For example, many governments around the world mandate that firms have to provide women with job guarantee during late pregnancy/mothers leave. Perks like these are costly and in competitive markets firms will just pass costs of these perks directly on their female employees (in form of lower wage - although this would not affect total compensation it is part of explanation for wage differentials).

Most labor markets are not perfect. In imperfect labor markets there are often quasi-rents to have that can be split between employee and employer. If for example women tend to be less assertive during negotiation (e.g. see Amanatullah & Morris, 2010), they might end up with less quasi-rents than their male counterparts (this argument is covered in greater detail in the Papayapap's answer).

The list above is not exclusive, but those are one of the major reasons you will commonly find in economics textbooks.
To sum it up, the argument you make in the question is valid argument but it  is only guaranteed to work under host of additional supporting assumptions. For example, assumption that customers do not care about gender might be true in some profession but not in others (e.g. perhaps male anchor appears more trustworthy to audience?).
Although careful studies that control for all relevant characteristics show that gender wage gap is much smaller than laymen commonly perceive it to be, based on senseless comparison of aggregates between male and female pay, many studies show that there still is some gender pay gap that cannot be accounted for by objective factors. See discussion of these results in this nice explainer from The Economist. For example, in 2016 the gender pay gap in the UK was estimated to be only $0.8\%$, in France$2.7\%$ and Germany $3\%$, once objective factors were accounted for. That is admittedly small, but should not be trivialized nonetheless.


CPI dataset from BLS I hope this question is not inconvenient for any of you, so I would appreciate if you do not mark it as not useful.
I want to work with the data of the CPI from the BLS (I am not American, and this is the first time I am using the BLS). I want to ask where I can find the raw data and the relative importance of components. My goal is to build some aggregate categories of my own.
Also, I would like to know if there exists any R script or Stata do file that explains how to build the CPI.
Thank you in advance for your help.

The BLS details the construction of the CPI Index in detail here
Moreover, you can find some notes on methodology changes here
You may find the package IndexNumR in R useful for index number calculation, you can find the documentation here


Where can I find research on the impact of informal/unregulated suppliers on prices and externalities? I am looking for research and papers on the impact of informal/unregulated suppliers on market prices and negative externalities. I am particularly interested in the manufacturing sector.

A quick way to do this would be through google scholar? Papers are then available via libgen or scilib
If you're part of an institution such as a university then you should have access to EBSCO Econlit which allows you to scan every economics paper using Boolean search strings to filter down to what you're looking for.


Variance of $\hat{\beta _0}$ in case of homoskedasticity Stock and Watson express the variance of $\hat{\beta _0}$ like
$\hat{\sigma }^2_\hat{\beta _0}=\frac{E({X_{i}}^{2})}{n\sigma _{X}^{2}}\sigma ^{2}$, but starting from variance of $\hat{\beta _1}=\frac{\sigma^{2}}{n\sigma_{X}^{2}}$ i proved only that $\hat{\sigma }^2_\hat{\beta _0}=\frac{1}{n}\sigma^{2}(1+\frac{\bar{X}^2}{\sigma_{X}^{2}})$, that is the same that is showed here.

How can i prove that are similar forms?

They are certainly not the same, since the first contains an expected value while the second does not contain an expected value but a sample mean.

Of course, they are very closely connected, and one can see this if one writes down the definition of the variance of a random variable.


MRS of good X for good Y I'm having some confusion with using the terminology of MRS. For example, let's take up this question:

  If good 1 is a “neutral,” what is its marginal rate of substitution for good 2?

The answer is:

  Zero—if you take away some of good 1, the consumer needs zero units
  of good 2 to compensate him for his loss.

The question is asking for the MRS of good 2. The answer interprets this question as "how much of good 2 will one need to give up for a loss in good 1?". In that case, it is, of course, 0. But, you can just as easily interpret the question as: "How much of good 1 will one need to give up for a loss in good 2?" -- in which case, the answer is infinity -- the consumer would not want to give up any amount of good 1 for a loss in good 2.

In a different question, this was asked: "What is your marginal rate of substitution of \$1 bills for \$5 bills?". And the answer was "5". So, in this question, the good after the word "of" (\$1 bills) is what you're giving up. In other words, the answer is the answer to the question: "How many \$1 bills would you give up for a \$5 bill?" So this answer interprets the question in completely the opposite way.

To phrase it more clearly, when someone asks:

  What is the MRS of good 1 for good 2?

How should I interpret it? Is it "how much good 2 you would give up for an increase in good 1?" or "How much good 1 you would give up for an increase in good 2?"

It is your second answer. From MWG:

  $$\frac{\partial u(x^*)/\partial x_\ell}{\partial u(x^*)/\partial_k}=\frac{p_\ell}{p_k}\tag{3.D.5}$$
  The expression on the left of $\text{(3.D.5)}$ is the marginal rate of substitution of good $\ell$ for good $k$ at $x^*$, $MRS_{\ell k}(x^*)$; it tells us the amount of good $k$ that the consumer must be given to compensate her for a one-unit marginal reduction in her consumption of good $\ell$.


Adverse selection problem The classic literature refers to the problem where information asymmetry exists between an informed and an uninformed counterpart as the adverse selection problem, but how can we verify what kind of such a game is? Namely, how can we know that such a game is a screening game or a signaling one?

Signaling is the informed side taking actions to reduce (or maintain, depending on the private types) the information asymmetry. For example, high skill workers getting certifications to signal their productivity so as earn a higher wage. Or low skill workers trying to mimic the high-skilled's behavior as much as possible so that they cannot be separated.

Screening is the uninformed side taking actions to reduce the information asymmetry. For example, monopolists use price discrimination strategies to identify customers with high vs low willingness to pay and charge them accordingly.


What are the concepts in Linear Algebra that model the idea of Identification Strategy in Econometrics? I just would like to know what concepts one should know before talking about identification strategies in econometrics. I see people studying such concepts but I'm not sure they realize (or even know) that probably there are concepts in Linear Algebra (or Multivariate Mathematical Analysis if that is the case) that probably would make their life easier had they known that beforehand. Since I'm currently studying a more rigorous approach to linear algebra (nothing fancy, but definitely proving everything), I would like to understand this "Identification Strategy" concept that I remember "learning" in an introductory econometrics course where I'm pretty sure no one even mentioned what was the math behind it, which I think is extremely important.

If you could be clear and didactic I would appreciate a lot.

Thank you!

In econometrics, identification can take several forms depending on the type of model you are working with (see this survey for a more comprehensive description). However, in general, identification pertains to the idea that either your econometric model or the exogenous variation in your data (or both) allows you to tease out the parameters that truly describe the data generating process.

To be more specific, suppose that you are studying outcome $y$ which depends (stochastically) on $x$ so that the data is generated according to $F(y|x)$ where $F$ is some distribution function. Then you have an econometric model that depends on a parameter, $\theta$ (this can be a vector of parameters), and for each parameter, it spells out a conditional distribution of $y$ in terms of $x$. That is your econometric model is a function $M(\theta)=G(y|x; \theta)$. We say that the model is identified if there exists a unique parameter, $\theta^*$, in the domain of $M$ such that $M(\theta^*)=F(y|x)$.

In reduced-form work, $M$ is assumed to be linear (or in general to be an injective function and have a relatively simple form), so ensuring identification has more to do with your data, $F$, having the right properties (mainly having enough exogenous variation).

In contrast, structural work typically deals with data that is not experimental or quasi-experimental so $M$ cannot credibly be assumed linear. Instead, you develop a rich enough model and identification requires $M$ to be invertible. If $M$ is invertible, then you have that $M^{-1}(F(y|x))=\theta^*$.

I think that in relation with identification, linear algebra tools are mainly useful to prove the invertibility of $M$. However, it can prove also useful when it comes to designing an estimation strategy, and when implementing the estimation strategy in the computer, etc.


Mean vs. variance - which is dominant? I am currently trying to gain some basic understanding of the mean-variance tradeoff. However, since I do not have an economic education background, I am struggling with some issues. Currently I am wondering which of the two is the key driver for decision-making. Let's say I have option A with a low variance and a low expected value, and option B with a moderate variance and a moderate expected value - which of the two would people choose? Are we more attracted by increasing profit or by relatively lower risks? Or is there no general principle and it depends on individual preferences?

I appreciate any input on that! Thank you in advance.

There is no general principle. It depends on preferences. Especially the degree of risk aversion, i.e. the preference toward risk.

As you mention, there is a trade-off. If there would be a dominant factor, as your title asks, then there would be no trade-off, as the best decision would always be clear.


How to estimate long term exchange rate? I'am looking for a book or paper that explain (in a theoretical and empirical way) how to estimate the long-run exchange rate for a country. Could someone suggest me one?

I kept digging and found the following references. Hope it will help someone else.

IMF: Estimation of Equilibrium Exchange Rates in the WAEMU: A Robustness Approach
IMF: Estimating the equilibrium real exchange rate: an application to Finland


What's the benefit the bank gets on offering me to pay my water/electricity/gas with credit card? It says "get extra 40 days to pay your bills" with no interest!

Is it just based on the statistics that people will eventually forget paying and then charge interests from those 40 days, or are there any other things they must be considering to offer this kind of service with no interest?

From a marketing point of view it's a very effective way of acquiring customers.

And you raise a good point, people often do forget these things, so banks can also profit that way.


Dynamic optimization with assets as state variable: interpreting capital gains and losses Given a hamiltonian of the form:
\begin{equation}
H_{t} = ln(c_{t}) \dot{} e ^{-\rho t} + \lambda_{t}(w+ra_{t}-c_{t}),
\end{equation}
with $c_{t}$ consumption at time t (the control variable), $\rho > 0$ time preference, $w$ a constant (for example wage), $a$ assets held in period t (this is the state variable for which the dynamics are described by: $\frac{da_{t}}{dt}=\dot{a_{t}}= w+ra_{t}-c_{t})$ and $r$ the return on assets.

The first order conditions are:

$\frac{\partial H_{t}}{\partial c_{t}}=0$

$\frac{\partial H_{t}}{\partial a_{t}}=-\dot{\lambda_{t}}$, or equivalently, $\frac{\partial H_{t}}{\partial a_{t}}+\dot{\lambda_{t}}=0$.

We were taught that $\frac{\partial H_{t}}{\partial a_{t}}$ can be interpreted as the marginal return on assets in t, and $\dot{\lambda}$ as a capital gain. Together they are the overal return. An accompanying sketch with $a_{t}$ on the horizontal and $H_{t}$ on the vertical axis shows $H_{t}$ is a concave function with a maximum.

The growthpath for consumption is given by $\frac{\dot{c_{t}}}{c_{t}}=-\frac{\dot{\lambda_{t}}}{\lambda_{t}}-\rho$, so that consumption grows over time if $\rho+\frac{\dot{\lambda_{t}}}{\lambda_{t}}<0$, that is: consumption grows if the relative capital losses exceed time preference. As such, the optimizing agent should let his consumption grow when there are sufficiently big capital losses.

My questions are the following:

Why can $\dot{\lambda_{t}}$ be interpreted as a capital gain?
If there are capital gains or losses ($\dot{\lambda_{t}} \neq 0$), how does this intuitively and/or graphically (in the $(a_{t},H_{t})$-diagram) effect optimization?
Why would consumption grow only in the case of capital losses? Is there an intuitive explanation?

Firstly, the $\lambda$ is by definition the shadow price of the wealth/capital. It is the price when you increase one unit of additional capital. $\lambda$ measures the rate of increase of the maximum value of the utility function as the constraint is relaxed. So, the dynamic equation of $\lambda$ is the change on this variable, which intuitively is the capital gain.

What do you mean by $(a_{t},H_{t})$ diagram ? Do you mean a phase diagram on $(a_{t},c_{t})$ ?

For your last question, you can realize on your constraint that the consumption increases at the expense of capital gain (c.f $\dot{a}$.) So, this means that every unit of consumption instead of accumulation is a somewhat loss in capital accumulation.


Government bonds - the most simple example I would like to have an example of a common case of buying government bonds. (Wikipedia: it is issued generally with a promise to pay periodic interest payments and to repay the face value on the maturity date).

What is the maturity date (years I guess), how much is that period of "periodic interest payments", how much is that interest, and what is the "face value"?

A simple example of the most common case. Or at least a known case of such a transaction. Say for example one of the latest government bonds issuing of Germany or Spain.

Say for example I buy German government bonds at a maturity date of N years, with a periodic interest of M months, and with the interest at P percent each month. Replace N, M, and P with a known case. In case I spend 100,000 Euros for such bonds, how much do I get back? (periodically, in the end and in total)

I think such example would make it easier for the common people like me to understand how government bonds work.

Using an example from the US treasury's website you can buy a \$100 dollar face value bond that pays 3% annual interest semiannually for thirty years. That means you would get $100 \cdot 3\% / 2 = 1.5$ dollars every six months, and at the end of the thirty years you also get \$100. In total you would get $1.5 \cdot 2 \cdot 30 + 100 = 190$ dollars but as it is explained in the link in Jamzy's comment adding up dollars in this case is not very sensible, because dollars in different years have different value.  (If you disagree I would love to trade with you. For 1000 dollars today I will happily give you 1000 dollars 10 years from now.)
 The price you pay for this bond is determined by auction. Using this you can calculate the yield of the bond. (Again, please see the explanation in the link.)
 The answer would follow the same logic for €100,000. You can convert the euros to dollars or find a suitable European bond.


Mechanism Design: Proving that the expected utility is differentiable Given a direct mechanism, we define a buyer's expected utility $u(\theta)$ conditional on her type being $\theta$ by $u(\theta)=\theta q(\theta)-t(\theta)$, where $q:[\underline{\theta},\bar{\theta}]\to[0,1]$ and $t:[\underline{\theta},\bar{\theta}]\to\mathbb{R}$.

We also define that a direct mechanism is incentive-compatible if truth-telling is optimal for every $\theta\in[\underline{\theta},\bar{\theta}]$, i.e., $$u(\theta)\geq \theta q(\theta')-t(\theta'),\quad\forall\theta,\theta'\in[\underline{\theta},\bar{\theta}].$$

LEMMA: For an incentive-compatible direct mechanism, we want to show that for all $\theta$ that $u$ is differentiable, we have $u'(\theta)=q(\theta)$.

PROOF: Consider any $\theta$ for which $u$ is differentiable. Let $\delta>0$. Then by incentive compatibility, we have the following:

I don't understand how from incentive compatibility it follows the first and second inequality 2.6 and 2.8. How can we use the same $\theta$ in the proof while the definition clearly states $u(\theta)\geq \theta q(\theta')-t(\theta'),\forall\theta,\theta'$?

By definition we have
$$
\begin{align*}
u(\theta) & = \theta q(\theta)-t(\theta) \\
\\
u(\theta + \delta) & = (\theta + \delta) q(\theta + \delta)-t(\theta + \delta)
\end{align*}
$$
By incentive compatibility (where $\theta + \delta$ is the true type, $\theta$ is the false type) we have
$$
u(\theta + \delta) \geq (\theta + \delta) q(\theta)-t(\theta)
$$
Using these (I used square brackets for clearer notation, there is no mathematical function to them)
$$
\begin{align*}
u(\theta + \delta) - u(\theta) & =
\left[(\theta + \delta) q(\theta + \delta)-t(\theta + \delta)\right] -
\left[\theta q(\theta)-t(\theta)\right]  \\
\\
u(\theta + \delta) - u(\theta) & \geq
\left[(\theta + \delta) q(\theta)-t(\theta)\right] -
\left[\theta q(\theta)-t(\theta)\right]
\end{align*}
$$
This also holds if you divide by $\delta > 0$.

Similar argument for 2.8., using true type $\theta - \delta$ instead of $\theta + \delta$.


How exactly does money supply causes inflation? I think there's consensus among economists that a growth in money supply (I'm not thinking in any particular type of money supply) causes inflation (CPI), at least, in the long run.
What I'm interested in is the exact mechanism for that phenomenon. How "printing more bills" makes the price of, say, milk in the local supermarket raise? How does the people who set prices know that there's more money in circulation than before so it's time for a raise?

Thank you in advance.

The view that money growth causes inflation may have been a consensus view during the (old) Monetarist era, but it is unclear whether that still holds.

(One somewhat related question here discusses whether inflation can occur without money supply growth - link)

As seen in the data around Quantitative Easing, we certainly see deviations between money supply growth and inflation "in the short term."

In the "long run" (which is what you asked about), it is unclear what causes what. Other than in Monetarist models (which are arguably no longer consensus), the money supply is determined by a portfolio allocation by the household sector. That is, people hold money instead of interest-bearing bonds for whatever reasons. What policymakers control is the policy rate of interest. Therefore, money growth is an outcome of the policy environment, like the inflation rate. In other words, faster nominal GDP growth (and hence inflation) tends to cause faster money supply growth.

However, if we want to assume that policy makers are controlling the money supply, and that money growth indeed causes inflation, modern theories would probably lean on the following two mechanisms.

Money does not just appear; it is a government liability, and the growth of government liabilities implies fiscal deficits. That is, loose fiscal policy raises demand, and causes inflation.
Expectations. If people are convinced that money supply growth causes inflation, they will watch the published money supply numbers. (In the United States, money numbers are published weekly, and were followed closely during the early 1980s. Currently, I doubt that 10% of bond traders under the age of 30 know what day of the week money numbers are published on.) They will raise prices based on what they think the money supply is doing now, and what they forecast it will be doing. I am not expert on Market Monetarism (the modern offshoot of "old Monetarism") but I believe that they emphasise this effect; although they might phrase things differently.


UK trade with biggest partners, by year I am working at lists like these, filling them with numbers about UK's trade with it's biggest partners. The problem is, I can't find such data for the years before 2010. I know that the institution (Office for National Statistics - ONS) changed it's website in 2009 or so, and maybe it has to do something with that, but I can't find the data on the older site also.

The OECD has nearly the exact data you are looking for. I haven't figured out how to get it into a raw data dump. I am pretty confident that this problem has more to do with me than it does with the data available.
The WTO holds similar quality information. I think somewhere a UK govt body is publishing this. Not sure where exactly though.
The UN has everything you need but you need to sign up.

See this question for other trade related sources:

Can anyone recommend a resource for monthly trade data for Mexico from before 2010?


Dealing with Missing Values in Diff-in-Diff Estimation To preface this, I am asking this question on the Econ SE because I was made aware on Cross Validated that Difference in Difference estimation is quite an economics specific method.

The picture above depicts deviations from Covered Interest Parity, for the Yen, the Euro and Pound Sterling. The vertical gray bars mark quarter-end reporting dates. My plan was now to apply a Difference in Difference estimation in order to investigate if larger deviations from CIP coincide with quarter ends after January 2015. The reason behind suspected quarter-end dynamics is the change in reporting standards for global banks in 2015. From 2015 onwards European banks are coerced to report a snapshot of their balance sheet on the last day of each quarter. For U.S. banks a similar report is required, however in the form of the average of the month ends. The reasoning behind this hypothesis is that said reporting standards coupled with capital requirements cause balance sheet costs and therefore lead to a decrease of banks engaging in arbitrage.

This paper by DU et. al., which applies a difference in difference estimation, investigates the above-explained dynamics:
https://onlinelibrary.wiley.com/doi/full/10.1111/jofi.1262

As can be seen in the graph the three time-series that I could acquire, have different missing values. Consequently, I am unsure of how to proceed.
The procedure applied in the paper does not seem like it actually requires a panel, please correct me here if you disagree. What leads me to believe that a panel is not actually required here, is based on the fact that the treated and untreated group are not as normally the case two different parts of the panel, in this case they are treated and untreated groups, at quarter-end/not at quarter-end and prior to 2015/after 2015 in all three time-series.

Hence this results in two questions:
Is it possible to build a panel considering that the three time-series have missing values at different points in time without losing every observation that is missing for one of the three currencies?

Is it possible to estimate this difference-in-difference for each currency by itself, considering the facts I have pointed out above?

Model:

$x_{1w,it}= \alpha_0 +\gamma_1Post15_t+ \beta_{1}QendW_{t}+\beta_{2}QendW_{t} \times  Post15_t + \epsilon_{it}$

Where $QendW$ equals 1 if the settlement date of the contract is within a reporting date, $Post15$ equals 1 after the European Leverage Ratio Delegated Act was established, and zero otherwise.

I am very grateful for any tips or hints, thank you!

Just to make sure I understand:

You have a daily panel (with missing values), probably weekday only, running from 2013 to mid-2017 with $n=3$ cross-sectional units.
You believe that after 2015, there is a stronger incentive to do something different at quarter end, so the average basis should be different on month end days during that period.

The typical difference-in-difference regression compares two types of units, some that are treated and some that are not, before and after the start of treatment. You don't have that here, but I think that's okay, because you are instead looking at the difference between quarter end and the rest of the quarter, pre- and post-.

As a general matter, this setup is fine. As long as the missing-ness of the data is "missing completely at random" or "missing at random". The problem occurs when the pattern of missing data reflects selection on the LHS variable, omitted variables causing changes in the LHS, or the value of the variable that's missing is related to the reason it is missing.

Another concern would be empty cases. You want to make sure that you have quarter end and quarter not end observations for the pre- and post-period. If you use of unit FE, you want to make sure that you have data pre-/post-, quarter end / not, for each unit.

Make sure you are also satisfying the requirements of the difference-in-difference setup. Some are un-testable, but at a minimum you should make a graphical or statistical argument that your data satisfy the common trend assumption.

Here is some sample Python code that shows that you can recover the initial parameters in your setup. I did it with month end, not quarter end, because it makes it easier to have a large enough sample of missing data for both difference variables.  It has an example of MAR and MCAR. It shows it works for both.

    import numpy as np
    import pandas as pd
    import statsmodels.formula.api as smf
    import itertools
    np.random.seed(9876789)

    startyear=2013
    endyear=2017
    daysinmonth = 21
    nummonths = 12
    numgroups = 3
    numyears = (endyear-startyear+1)
    dropfraction = 0.20
    # np.sort(np.tile(np.arange(2013,2018), 12*21))
    years   = np.repeat(np.arange(startyear,endyear+1), nummonths*daysinmonth*numgroups )
    days    = np.tile(np.arange(1,daysinmonth+1), nummonths*numyears*numgroups )
    months  = np.tile(np.repeat(np.arange(1,nummonths+1), daysinmonth),  numyears*numgroups)
    groups  = np.tile(np.repeat(np.arange(1,(numgroups+1)),nummonths*daysinmonth), numyears)
    df = pd.DataFrame({'year': years, 'month': months, 'day': days, 'group': groups})

    # Last weekday is monthend or the 21st
    df['endofmonth'] = df.day == 21
    df['QendW'] = 1*df['endofmonth']
    df['Post15'] = 1* (df.year > 2015)
    df['random'] = np.random.rand(daysinmonth*nummonths*numyears*numgroups)
    df['missing_completely_at_random'] = df['random'] <= dropfraction
    df['missing_at_random'] = (df['random'] - 0.05 * df['Post15'])  <= dropfraction
    df['QendWxPost15'] = df['QendW'] * df['Post15']

    alpha0 =  0.2
    gamma1 =  0.05
    beta1  = -0.10
    beta2  =  0.15
    df['error'] = np.random.randn(daysinmonth*nummonths*numyears*numgroups) / 10

    df['x1'] = alpha0 + gamma1 * df['Post15'] + beta1 * df['QendW'] + beta2 * df['QendWxPost15'] +  df['error']

    df['x1_missing_completely_at_random'] = df['x1'].copy()
    df.loc[df['missing_completely_at_random'] == True, 'x1_missing_completely_at_random'] = np.nan

    df['x1_missing_at_random'] = df['x1'].copy()
    df.loc[df['missing_at_random'] == True, 'x1_missing_at_random'] = np.nan

    print("No missing data")
    model = smf.ols(formula='x1 ~ Post15 + QendW + QendWxPost15', data=df)
    results = model.fit()
    print(results.summary())

    print("Missing Completely At Random (MCAR)")
    model_missing_completely_at_random = smf.ols(formula='x1_missing_completely_at_random ~ Post15 + QendW + QendWxPost15', data=df)
    results_missing_completely_at_random = model_missing_completely_at_random.fit()
    print(results_missing_completely_at_random.summary())

    print("Number missing quarter end (MCAR)")
    print(np.sum(df.loc[df['missing_completely_at_random'] == True, 'QendW'] ))
    print("Number missing quarter end post 2015 (CAR)")
    print(np.sum(df.loc[df['missing_completely_at_random'] == True, 'QendWxPost15'] ))

    print("Missing At Random (MAR)")
    model_missing_at_random = smf.ols(formula='x1_missing_at_random ~ Post15 + QendW + QendWxPost15', data=df)
    results_missing_at_random = model_missing_at_random.fit()
    print(results_missing_at_random.summary())

    print("Number missing quarter end (MAR)")
    print(np.sum(df.loc[df['missing_at_random'] == True, 'QendW'] ))
    print("Number missing quarter end post 2015 (MAR)")
    print(np.sum(df.loc[df['missing_at_random'] == True, 'QendWxPost15'] ))


Diminishing MRTS range So I was solving a question which says find the range in which MRTS is diminishing
$f(k,l) = 600k^2l^2-k^3l^3$ is the production function
I got the MRTS = $ -(1200kl^2-3k^2l^3) \over 1200k^2l-3k^3l^2$

Now, i'm getting $\frac{\partial f'(k,l)}{\partial k} = \frac{l}{k^2}$ then range is $l <0 $ which doesn't seem to make sense.
Is my answer correct or is there some mistake??

Fix the production function to a constant value $f_0$ so that

$$
f_0 = 600k^2l^2 - k^3 l^3 \tag{1}
$$

This is a figure for different values of $f_0$ (isoquants)

Note that with increasing $k$ the rate of change of $l$ becomes smaller for a given $f$ and a fixed change in $k$. We can show this more formally by taking the derivative w.r.t $l$ at both sides

$$
0 = 600\left(2kl^2 + 2k^2 l \frac{\partial l}{\partial k}\right) - \left(3k^2l^3 + 3k^3l^2\frac{\partial l}{\partial k}\right) \tag{2}
$$

You can further simplify this to

$$
0 = -3k l (-400 + k l)\left(l + k \frac{\partial l}{\partial k}\right) \tag{3}
$$

The solutions are $k = 0$, $l = 0$, $kl = 400$ or

$$
\frac{\partial l}{\partial k} = -\frac{l}{k} \tag{4}
$$

Which confirms that that increasing $k$ decreases the MRTS, in other words, the range in which the marginal diminishes is $k > 0$


When can tit-for-tat be sustained? Consider the following infinitely repeated hawk-dove game:

$\hspace{1cm}\hspace{1cm}\hspace{1cm}$

For what discount rate $\delta$ can tit for tat sustain $D,D$? My professor says the answer is $\delta=1$ (discounts are applied by multiplication e.g. payoff of 1 in the next period yields $\delta$).

It seems to me like two cases need to be considered:

1) Suppose we start with $D,D$, then tit-for-tat sustains this $\iff$ $\frac{4}{1-\delta}>8+\frac{\delta}{1-\delta} \iff \delta>4/7$.

2) Suppose we start at $H,H$, under tit-for-tat, player 1 would restore $D,D$ if $\frac{1}{1-\delta}<0+\frac{4\delta}{1-\delta} \iff \delta>1/4$.

What am I doing wrong?

History $(D,D)$ (Yes, this is with some abuse of notation.):

On Path: Each player gets an average payoff of $4$.

Deviating: Say player $1$ deviates and instead chooses $H$. Then the resulting sequence of actions is the alternating sequence, $(H,D)$, $(D,H)$, $(H,D)$,...

Accordingly, player $1$’s sequence of payoffs is $(8,0)$, $(0,8)$, $8,0$... Player $1$'s average payoff is $(1-\delta)\big(8 + 0 + \delta^{2}8 + 0 + \delta^{4}8 + \cdots\big)$, which is $$(1-\delta)\frac{8}{1-\delta^{2}}=(1-\delta)\frac{8}{(1-\delta)(1+\delta)}=\frac{8}{1+\delta}$$
Hence, there is no profitable deviation here iff

$$\begin{split}
4 &\geq \frac{8}{1+\delta}\\
4\delta &\geq 4\\
\delta &\geq 1
\end{split}$$

Thus, $\delta \geq 1$ is a necessary condition (not necessarily sufficient because we still need to check that following tit-for-tat is still optimal for the other histories. I'll leave that to you!)


Absolute vs Relative Risk Aversion Are there results that says the monotonicity of one measure of risk aversion implies the monotonicity of the other measure?

For example,

Does constant relative risk aversion imply decreasing absolute risk aversion?
Does constant absolute risk aversion imply decreasing relative risk aversion?

and so on.

Given a utility function $u(c)$, $c>0$, with $u'(c)>0, u''(c) <0$. Regarding the sign of the second derivative, if it is zero, then both measures are zero, if it is positive, it would imply increasing marginal utility, and I don't remember having seen these attitude-towards-risk measures for such a utility function.

Denote Absolute Risk Aversion ($ARA$) and Relative Risk Aversion ($RRA$) correspondingly by

$$A(c) = -\frac {u''(c)}{u'(c)},\;\;\; R(c) = cA(c), \;\; A(c) = \frac 1c R(c)$$

1) Monotonicity of $A(c)$ in relation to $R(c)$

$$\frac {\partial A(c)}{\partial c} = \frac {\partial [(1/c)R(c)]}{\partial c}= -\frac 1{c^2} R(c) + \frac 1{c}\frac {\partial R(c)}{\partial c} $$

So if
$$\frac {\partial R(c)}{\partial c} \leq 0 \Rightarrow \frac {\partial A(c)}{\partial c} < 0$$

$RRA$ weakly decreasing  $\Rightarrow ARA$ is strictly decreasing in $c$.

2) Monotonicity of $R(c)$ in relation to $A(c)$

$$\frac {\partial R(c)}{\partial c} = \frac {\partial [cA(c)]}{\partial c}=A(c) + c\frac {\partial A(c)}{\partial c} $$

So if $$\frac {\partial A(c)}{\partial c} \geq 0 \Rightarrow \frac {\partial R(c)}{\partial c} > 0$$

$ARA$ weakly increasing $\Rightarrow RRA$ is strictly increasing in $c$.


Problems with economic modelling: good textbooks/articles/papers? I am interested in the problems that may arise when one models economical phenomena using mathematics and indeed also the nature of these models. Are there some good and easily accessible textbooks, articles, papers or other literature out there treat this topic? Perhaps a few classics that are well-regarded in the field?

My background is mostly mathematical, but I understand intermediate economics, both macro and micro.

I recommend reading Dani Rodrik's new book, Economics Rules: The Rights and Wrongs of the Dismal Science. Rodrik supports the use of mathematics as bringing clarity of meaning, and argues that the subject is far more applied and empirical than its detractors realize.

The final couple of pages have Rodrik's Ten Commandments for Economists. Numbers one and two are: Economics is a collection of models; It's a model, not the model. And also Ten Commandments for Non-Economists, which include: maths is useful; economists are not all alike; economists typically do understand how markets work.

All the Commandements with Dani Rodrik's comments can be found here. This review can also be useful for clarifying some of the commandments.


How can a RTA have a trade imbalance with itself? I'm looking at ASEAN data for 2015. It seems the intra-ASEAN exports are 305bn USD while the intra-ASEAN imports are only 238 billion USD. Am I misreading the statistics or is it actually possible for a group of countries to have such an imbalance in the group?

Yes, such imbalances or asymmetries are possible in practice and quite frequent across RTAs. These statistics simply mean that each ASEAN member exports more on average to the other members than they import from them. But this pattern is not true for all ASEAN countries.

Singapore exports 118bn USD to the other ASEAN countries and imports 64bn USD from them.
Mynamar exports 4bn to the other ASEAN countries and and imports 7bn USD from them.

Finally, they define the `intra-ASEAN exports' as the sum of the exports of the individual ASEAN countries (305 billion USD in the second column of ASEAN data).

In theory, this number should be equal to the sum of the individual imports from the other ASEAN members. To fix ideas, consider a RTA with 3 countries A,B, and C. If each country exports 10 to each partner. The sum of exports is equal to 60, and if A->B is equal to B<-A, etc. the sum of imports is also equal to 60.

In practice, this number is not equal because intra-RTA exports may not be calculated the same way as intra-RTA imports. This is explained in this Eurostat website for the intra-EU trade.

Asymmetries occur when the declaration of the importer in country A is not consistent with the declaration of the exporter in country B. Asymmetries come either from errors in reporting or from differences in the concepts and definitions applied by the partner countries. Some of the asymmetries of a methodological nature could disappear thanks to further harmonisation in national practices; others will continue as they are linked to basic principles for compiling ITGS which are unlikely to change (e.g. CIF/FOB valuation). The most common causes of methodological asymmetries are the following:

simplified product reporting: Where the EU legislation allows simplified codification of goods for certain transactions, some Member States apply the simplifications but others do not;
different approaches to estimating or compiling trade data for specific goods and movements not covered by customs or statistical declarations, such as gas, electricity, vessels or aircraft;
confidentiality: It is possible that data are considered confidential by only one of the two partners. However, the application of the confidentiality procedures should not impact the results at total trade level;
time lag: the same operation can be recorded under a different reference period because of transport times or processing delays;
CIF/FOB valuation: imports are valued on a CIF basis and exports on a FOB basis. This causes a systematic asymmetry as the value of the imports should then be higher than the value of the mirror exports;
differences in methods and data used to estimate missing trade;
different practices in the treatment of revisions;
problems of currency conversion; and
other methodological differences such as definition of partner country, definition of statistical territory, trade system (special or general).

Why is the intra-EU trade balance not equal to zero?

Considering that the intra-EU trade data are based on common and largely harmonised rules, one might expect the intra-EU trade balance to be zero or at least close to it. However, it is worth underlining that a perfect match is made impossible first of all by the CIF/FOB approach: the import value should be higher than the mirror export value as it includes extra transport costs.

What is the 'Rotterdam effect'?

Dutch trade flows are over-estimated because of the so-called ‘Rotterdam effect’ (or quasi-transit trade): that is goods bound for other EU countries arrive in Dutch ports and, according to EU rules, are recorded as extra-EU imports by the Netherlands (the country where goods are released for free circulation). This in turn increases the intra-EU flows from the Netherlands to those Member States to which the goods are re-exported. To a lesser extent, Belgian figures are similarly overestimated.


Trouble understanding hamilton 1996 specification testing in MS I'm an undergraduate student and I'm trying to code the specification testing for Markov-switching model in R, based on the article "Specification Testing in Markov-Switching time series models" Hamilton 1996.

More specifically, I don't understand equation 3.12, or maybe I have misunderstood something.

In equation 3.12 you have a term like this

$$ = p_{ij}^{-1} p(s_{t}=j,s_{t-1}=i|\Omega_{t})$$

But since

$$ p(s_{t}=j,s_{t-1}=i|\Omega_{t}) = p_{ij} p(s_{t-1}=i|\Omega_{t})$$

then

$$= p_{ij}^{-1}p_{ij} p(s_{t-1}=i|\Omega_{t}) = p(s_{t-1}=i|\Omega_{t})$$

Is this ok?

In my question, I rewrite the joint probability as the product of the conditionals probabilities but I wrongly implemented the markov assumption of this model. The markov assumption is:
$$
p(s_{t}=j|s_{t-1}=i,\Omega_{t-1})=p(s_{t}=j|s_{t-1}=i)=p_{ij}
$$
But, when conditioned to information available at date t
$$
p(s_{t}=j|s_{t-1}=i,\Omega_{t})\neq p(s_{t}=j|s_{t-1}=i)=p_{ij}
$$
Meaning that

$$
p(s_{t}=j,s_{t-1}=i|\Omega_{t})\neq p(s_{t}=j|s_{t-1}=i)p(s_{t-1}=i|\Omega_{t})
$$


Finding a Research Project A professor at my college (I'm a rising sophomore) expressed interest in me working for him as an RA as I have had previous research experience prior to coming to college and I did well my freshman year. He told me that he has projects that he is working on, but what he would enjoy more is for me to come to him with an interesting project idea and then he will just pay me to go loose on the work and come to him when I get stuck. In all my prior research experience I've always been assigned a research question or topic and from experience with open-ended classroom assignments I always struggle with finding a good topic and having that topic satiate my interest for a long time. I was wondering if you guys had any pointers as to how to come up with an interesting problem or research question. Furthermore, I want to do some work in financial trading, or quantitative finance or some field that utilizes heavy amounts of modeling and programming so if you know of any open research problems in those fields, feel free!

One of the discussions in financial forecasting is whether econometric methods or machine learning methods give more sound results. Have a look at this paper.


Why would China's housing be in a bubble? This popular video on YouTube, "China's Reckoning: Housing Crisis", suggests that the housing market in China is in a bubble, because the vacancy rate is very high.
However, it also acknowledges that there is no credit bubble like there was in the US.
I don't understand this argument. As the video claims, the Chinese are culturally "obsessed" with real estate, and buy houses even though no one lives in them, which drives up the price of real estate.
However, isn't this exactly what we have with gold? Most gold is not being used for anything productive. It's our cultural obsession with gold that keeps up its price. No one suggests that gold is in a bubble because of this.

This issue has been at the fore among China watchers for many years. While bubble classification is often subjective (even the Fed concedes):

[The] econometric detection of asset price bubbles cannot be achieved
with a satisfactory degree of certainty.

However, there is mainstream consensus that China's real estate sector likely has cause for concern. The below are not intended to be smoking guns, but may be useful heuristics for intuition：

Property developers are typically among the most leveraged operations
in China (leverage has been associated with previous "bubbles" like
US in 1929 and 2000.)
As you mentioned, property transactions as a result of speculation is
increasing versus organic demand from end-users of property
(also have divorce legal loopholes to acquire more real estate)

China began a deleveraging process several years ago but rebooting the economy after economic shutdown in 1Q20 required stimulus. However, recently, PBoC has been tapering liquidity after a banner year in financial assets and even some alternative assets (like real estate). As mentioned, leveraged companies often feel the brunt of a tightened liquidity environment first. China Fortune Land was perhaps the first casualty, defaulting on billions of dollars in 4Q20. A few days ago (at the time of writing) the financial media also picked up a similar situation unfolding with an even larger developer:
https://www.bloomberg.com/news/articles/2021-03-25/rating-cut-ignites-fresh-concern-over-chinese-real-estate-firms
Housing vs credit vs gold
To get at your question more directly, I think it's still fair to say that credit is at play here. The latest macro leverage figure from December 2020 prints 270% of GDP. (M6404532 is the code if you have a Wind financial terminal). Ultimately, this number plays a large role in guiding policy decisions. In the last year alone, property restrictions and land developer bond financing rules were tightened in an attempt to bring leverage down and cool the sector as a whole. Still, there is a lot of nuance and market quarks to be aware of here; this is where the 'art of policy' comes into play.
On the one hand, regulators want to preemptively de-risk the financial system and develeraging is one of several paths forward.
However on the other hand, real estate is a vital part of the fiscal income of many regional governments -- many of which have very troubled financial situations. Many provincial governments turn to non-standard credit to bolster their fiscal budget: real estate developers and other local SOEs are often the issuers. Global ratings agencies are not currently allowed to rate these local government financing vehicles, and local rating agencies tend to be rather generous, so this could explain partially the necessity and propensity of a 'bubble' to emerge and sustain itself.
The notion of 'cultural obsession' has its role too. After all, capital markets are somewhat new to China; the SSE has only been around for 30 years. We can see that the median investor portfolio tilts towards real estate more so than most:

However, after we peel back the financial engineering, China's housing bubble is not so different from the US experience. Or, it could arguably be characterized as a combination of cultural obsession and leverage.
Let's try and tie this to gold. Any bubble, again, as the Fed has conceded, is subjective. However, if we look at what prices reflect on the various portions of the economy, you could find conflicting signals.

In a reflationary scenario, or if deleveraging turns out to be
successful, the real estate and financial markets could march forward
into the next few years unscathed. This might suggest the bubble was
in gold.
Alternatively, if we view gold as a safe-haven asset and succeeds in
preserving wealth after a vicious correction, then life in a
rear-view mirror makes the bubble appear to be in assets (financials/alts).

Ultimately, if we subscribe to the belief that capital markets produce informative prices, then the prices in these different asset classes could reflect diverging views of the direction of the economy. Still, in a world of  momentum traders and Fed interventions, fundamentals aren't the only thing at play. For this reason we should pay attention to who is investing with stewardship or high-conviction from those who are trying to turn a quick buck. I'm sure there are plenty of speculators in the gold market.


Nominal and Real GDP If nominal GDP is, for example, \$1,620 billion in 2017 and increases to \$1,680 billion in 2018, is the growth rate 3.7%? Or does real GDP first need to be calculated using a deflator?

As you pointed out, you need to distinguish between real and nominal GDP. The 3.7% growth from 1620 to 1680 is the nominal growth. To figure out real growth, you need to know the price index of the two years.


Is stock investment an example of demand curves that slope upwards? The law of demand implies that when the price of a good rises, people buy less of it. This makes the demand curve slope monotonically downwards. A textbook exception is the so-called Giffen good that by definition behaves in the opposite way.

But take a stock on the rise; might there not at least be one tendency for people to buy more of it under some circumstances, because it looks promising, in spite of any opposite tendencies? Is or isn't this another case where a demand curve may slope upwards?

The observation that "as stock prices rise people tend to buy more of it, so here the "law of demand" holds in reverse", is one of the more widespread misconceptions related to economic thinking.

To expand on @StevenLandsburg comment, the "demand curve" (or "schedule") describes a static relation between the quantity of a good and its price. Its quality (in the general sense of features, qualities) remains constant, as we trace the demand curve.

In our case we think of the stock as the good, and we have also its price. Now what happens when the price goes up? Are we talking about the same good? Definitely no. The price raise itself signals that we now have a better good (higher discounted future profits). But then we have a different good.
So the pairs $(p_1, q_1)$ and $(p_2, q_2)$ do not really belong to the same demand schedule. So the "law of demand" is irrelevant here as a tool to describe the situation (we could picture it as a shift of the whole demand schedule outwards)

If one wants to try to think "intuitively" about the law of demand and stocks, then one can consider the following thought experiment:
Assume that you can buy the same stock on the same day at two different prices (it can happen, the world is not really one single market, even for stocks -and transactions can happen also outside of the stock market).
Assume that you cannot choose the seller, which will be determined randomly. But you have to commit beforehand about the number of pieces that you will buy. And you know the two prices.

What are you going to do?
Commit to buy more pieces if you are dealt the expensive seller, and fewer pieces if you are dealt the cheaper seller? Sounds ridiculously obvious, isn't it?


Why are the 10 year German bunds and 10 year US treasury yield highly correlated? And when the ECB started QE this year, the 10 year bunds rate naturally plummetted, but so did the 10 year US treasury rate. What fundamental reason could cause the US rate to drop in synchrony?

Because they are the so called "Fly to Quality". When markets turmoil investors look where to safely invest their money to avoid volatility and risk. This lead to a depreciation (because of the more demand in bonds, the less bond rate) in rates. After the QE, as you said, the left risk about the already solid Germany was cut out because of the unlimited coverage by the ECB on European Bonds. Germany and US are considered the two solid major economics of the World and every rational investor who wants to do not run too risk (because it is impossible to avoid it entirely, see "idiosyncratic risk" for further information) prefers to invest there. A similar but slightly different chapter is for gold, considered a "Fly-to-Quality" good as well. Concluding, part of the correlation is due to economic relationships that there are between US and EU and it also accentuates the effects of important macroeconomic news of one or the other party.

TIP

Do you know why, sometimes, it is possible to see negative rates (as Danish or Switzerland or some Scandinavians)? Because sometimes, even if the rate is negative (you have to pay the issuer) the cost of carry is higher and then investors just leave their money there, paying less than to disinvest and invest again in another kind of security. This happened for some days to German rates too!


Negative correlation conditional variance and return I've estimated a GARCH for S&P 500, Nikkei and DAX index.

The model for the return of S&P 500, the results indicate the return of DAX has an negative effect on the S&P 500 conditional variance?

This is not in line with what I expected, given that high volatily must give a higher return.

Can anyone help with this?

This is simply saying that the falling returns are usually correlated with high variance (i.e. sharp movement in prices). This is not very surprising... you can see from the graph of S&P vs VIX below. Whenever the stock market plunges, volatility shoots up.

What @heh said was right. You need to compare assets that are more or less substitutable. For example, for what you said to hold true, it must be that investors in the US could say, returns in the US relative to the volatility is pretty low, so I'll move my money to Germany (where returns relative to volatility is higher) and invest there instead. In reality, such investors would have to face with interest rate differentials, FX risks, etc.

The "high volatility -> high return" applies to assets that are in the same "portfolio" (for me, this means in the same country at least). For example, you could say that a stock of a small company offers very high return but is very risky. You then have another asset, of a large company, that offers low return but is more stable. You also have the "risk-free" treasury bill that offers the lowest return and no risk at all.


Is human psychology really reflected into inflation? I am a total noob in economics, but I'm trying to understand why do we have inflation. I can see that prices are increasing over time and, by definition, this means inflation. I'm fine with this definition, but I don't understand why we have it everywhere in the world.

Once, someone told me that this is caused by our psychology and that we kind of "get used" to something, and it doesn't have the same value like before, and I was wondering if this is right. Do money depreciate because we give it a less value over time or what is the fundamental reason for inflation? Or is it more complicated than my kindergarten understanding of it?

I can imagine that this is a stupid question for most people, but for me, it is very important to understand it at the fundamental level.

Economics is, first and foremost, a discipline that tries to understand human behavior, in particular human behavior regarding scarce resources and choices over them. You are correct in doubting a "we kind of get used to it" explanation if you want an economic explanation. While people may give a lot of reasons to why they make prices behave the way they do, empirically they behave as acting upon the laws below.

Inflation has been plaguing humanity since it developed coinage (barter and money-less economies can't have inflation) and many theories have been developed to explain it. Causes at a fundamental level have changed as economic theories came and went, but there's a consensus among most modern economists.

Without going too much into details, money itself is a good, and as such it's influenced by basic laws of supply and demand. If people desire more money to spend on goods, its value will go up (and the price of things in term of money will go down); if people desire less money, its value will go down (and the price of things in terms of money will go up). In the long term, inflation is a purely monetary phenomenon: more/less money around means more/less supply and more/less goods around to spend money on mean more/less money demand. In the short term, other effects dominate, such as:

If people expect prices to go up, for whatever reasons, they'll mark up prices in a self-fulfilling prophecy. That's why part of a central bank's job is aligning expectations.
Certain prices, like wages, don't update frequently (especially if they ought to go down to reach equilibrium). This is one of the main reasons most central banks strive to reach a ~2% inflation rate: instead of getting wages down by increasing unemployment, they lower wages to equilibrium across the board.
There can be a spiral were firms raise prices and workers demand higher wages to pay for those prices, and firms need to rise prices to pay for wages...
Around Christmas, people buy more. Central banks know this and put more money on the market to account for this extra demand.
What constitutes money is a hard question to answer, but in general it depends on how easy it is to buy goods with it. More money that no one spends won't imply more supply. This means you can print all the money you want and have no extra inflation... if you don't spend it.
If there's a systemic effect, like higher gas prices or exchange rates, a lot of prices will go up to align with lower purchasing power for a country. This is why central bankers tend to focus on "core" inflation instead of actual inflation.

You can see that it has a quite complex dynamics, but I hope the above will give you an intuition behind how to think about inflation.


Optimisation and Theory of Comparative Advantage In my school's lecture notes on International Trade, a numerical example of how the Theory of Comparative Advantage is featured. It is a simplified 2-country, 2-goods model and features constant opportunity costs.

  The US is able to produce either 200 kg of wheat or 120 m of cloth while China is able to produce either 10 kg of wheat or 20 m of cloth. That means, the US has an absolute advantage over China in the production of bosth wheat and cloth. However, the US has a comparative advantage in the production of wheat, sacrificing only 0.6 m of cloth for each 1 kg, while China has a comparative advantage in the production of cloth, sacrificing only 0.5 kg of wheat for each 1 m. Initially, each country allocates equal amounts of resources to the production of each commodity: US produces 100 kg of wheat and 60 m of cloth while China produces 5 kg of wheat and 20 m of cloth. However, this arrangement is not optimal. China specialises completely in the production of cloth, producing 20 m of cloth while the US engages in partial specialisation, producing 110 kg of wheat and 54 m of cloth. They then engage in trade with an exchange ratio of 1 kg of wheat to 1 m of cloth. China gives the US 10 m of cloth while the US gives China 10 kg of wheat. The outcome of specialisation and exchange is that China is able to enjoy more wheat while the US is able to enjoy more cloth.

However, I would like to find out what is the optimal production and trading arrangement such that both countries stand to gain the greatest amount of output overall for consumption. I am not sure how the numbers were chosen and why certain decisions were made (e.g. China completely specialising in cloth production, US diverting a tenth of its resources from cloth production to wheat production). Can calculus be used to find out what is indeed the most optimal arrangement?

Without more information on producers (such as their production functions) and consumers (their utility functions), we cannot compute the optimal levels of production and trade

The point of this simple numerical example is merely to illustrate the key and surprising insight behind the principle of comparative advantage: Even if the US is better than China at producing everything, both countries (including the US) can still benefit from trade.

The numbers were somewhat arbitrarily chosen (this is a point sharp students are often confused by and the teacher should have mentioned this). They could be tweaked and still successfully illustrate the principle of comparative advantage.


Evaluate value added of a company i  have the value  added  of  a  company for 2 years  2016  and  2017  so  to  compare these  values and to see its evolutioon i divided the value of 2017 by  the one of  2016

but  some friends told me that the comparison i am doing is false because  values should  be calculated using  same  price  base

so  what should i do to have a correct  evaluation  ?

thanks a lot  in advance for  any help

What you are missing is the concept of GDP deflator .
Which is there while studying National Income Accounting.

When you are comparing change in Valve Added (GDP) of 2016 and 2017 , the change could have been mainly due to two reasons :-

Increase in production
or Change in prices

While comparing Value Added by the company in two years , second reason i.e change in prices would give us false result as while comparing GDP we want to see how much the production has increased/decreased between the years , but the change in prices might show an exaggerated value of GDP if we do not take into account the change in prices .

For getting the correct answer what we do is that we take one year as base year(generally the first year given to us i.e 2016 in your case) and calculate the GDP on the basis of the prices prevalent on that year , so that we get a true picture of real change in GDP .

$$
\text{GDP Deflator} = \frac{\text {Nominal  GDP}}{\text{Real GDP}} \cdot {100}
$$

Where Nominal GDP is the GDP of 2017 at current year i.e 2017 prices
And Real GDP is the GDP of 2017 calculated at base year prices i.e 2016

Also remember the formulae $\text{GDP = Units produced * Price per unit} $

Do refer to the following hyperlink

GDP deflator

Hope you find my answer useful. :)