How does buying bonds reduce bond yield? I read that Japan rapidly increased the printing of Yen and used the money to buy government bonds. How did this action reduce the yields of their government bonds and prevent them from having a debt crisis like Greece?

There are two legs to this question.

How did this action reduce the yields of their government bonds

The price of a bond and its yield are inversely related: if the price goes up, the yield goes down, by definition.
The assumption is that increased purchases by the central bank raises their price, by using supply/demand logic. However, these supply/demand arguments implicitly assume that bond traders are automatons who cannot adjust their selling prices. As such, there can be a debate about how much bond yields fall because of central bank purchases.

and prevent them from having a debt crisis like Greece?

The situations of Greece and Japan are different. The Japanese governments borrows (almost) exclusively in yen, a currency the government controls. (They might have a token amount of foreign currency borrowing, like many other developed countries. Even if they do, the size of the borrowing programme is much smaller than their foreign currency reserves.) The Bank of Japan effectively endures that the Japanese government can issue new bonds near its policy rate.
Conversely, the Greek government (mainly) borrows in euros, but the central bank for the euro is the ECB. The ECB let Greek bonds yields to decouple from other euro bond yields, which forced the Greek government to default.


Find Pareto optimal allocations and the core for the following economies Find Pareto optimal allocations and the core for the following economies.
There are two consumers and two goods. Utility functions are $u_1(x_1,y_1)= 10x_1-(y_1-2)^2$ and $u_2(x_2,y_2) = 10y_2 − (x_2 − 2)^2$. The endowments are (0,4) for agent 1 and (4,0) for agent 2.
I have its intuitive solution but I don’t understand this.

Please help me to provide a solution by calculation. Thanks a lot.

In the economy described above, set of efficient allocations is given by the red curve. Just do the slope of ICs comparisons at the boundaries and you will get that.


Why do yield spikes reflect badly on tech stocks Yield spikes are a reflection of market equity valuation.
How I interpret this is that there’s real fear that some companies in the market won’t be able to pay their debt. Therefore, bonds trade cheap, hence spiking the yield.
Large Tech companies (FANGs) are not going to default on their debt.... so why does yield spikes impact their stock value?

You are referring to recent market events where the yield of US Treasury bonds has risen and some tech stocks have been falling?   My interpretation of this situation is that the rise in yields reflects growing optimism in the broader economy , as we overcome Covid and head back to work.  In the stock market, this leads to a preference for traditional stocks that do well in a ‘normal’ economy, and a rotation away from the tech stocks that did especially well in the pandemic.  This we have a correlation rather than a causation.


What would be a an accurate metric of the real economy without the financial services sector? A youtube video by RT predicts that China's economy will surpass the US in GDP by 2028. The conclusion was a result of projections of GDP growth for both nation's economies. According to anearlier youtube video by CBS News, China already passed the US in purchasing power, another economic metric, according to the IMF, back in 2014.
Due to the size of China's manufacturing sector, which is the sector that actually produces real products and stable jobs, it seems that China has long ago passed the US in the real economy of real goods and services.  Is there a metric of the real economy without the obfuscations of the so-called financial services sector which includes banking, insurance, securities, intangible assets, derivatives, incomes derived from interest and speculation, etc?   In other words, a gross measure of the sum of transactions of tangible goods and services, not including any financing for the tangible goods and services.  Preferably the metric would be in tons of gold, silver, or other real substances of intrinsic value, not in US Dollars or other fiat currency.  Thank you.

This question is difficult to answer. You can start by simply finding the value of the manufacturing sector of any country's economy. But this quickly becomes very hard to define. Let's take Tesla for instance, Tesla manufactures cars, but they also are deeply involved in software engineering for their company. Is Tesla a software firm or a manufacturing firm?
The question gets even more complicated when you take a company which manufactures some of its own supply chain and also has investments in other companies, which will include basically every manufacturer in America, because almost any firm in the world will diversify its holdings when it has extra cash on hand.
The best answer you have will be to look at the latest GDP report and then subtract whichever sectors you don't consider "real"... which is available here for the United States: https://www.bea.gov/data/gdp/gross-domestic-product
But given how many large companies have financial investments as part of their corporation you have to have a good definition for what a "real economy" is in reality, which is not an easy question in your terms. Ultimately it is all real, just as real as the value of intellectual property such as patents or copyrights.
If you don't want to measure it in dollars you can always use xe.com to calculate the value in gold. Nobody measures economic data in gold, silver, or oxen nowadays.


On the relationship between income distribution and GDP I was thinking about the following simple example when I wondered what the theoretical effects wealth equality or inequality may have on GDP:

Suppose there is a society with three individuals who have enough money to cater for all their needs, and there is an additional 600 units of disposable income left over.

Let us assume that $S_i$ is the disposable income allocated to person $i$ and let us first assume person $i$ will then spend an amount proportional to $\sqrt{S_i}$ (This assumption is incorrect, but I'm just using it as an example of a non-linear income to expenditure relationship example, which I will compare below with a linear assumption), that is, disposable expenditure would be given by $k\sqrt{S_i}$ for some constant k.

Now if all the excess income was allocated equally among the three members, the total excess expenditure is $k(\sqrt{200} + \sqrt{200} + \sqrt{200}) = 42.43k$.

However if all of the excess income is allocated to a single individual, the total excess expenditure is $k\sqrt{600} = 24.49k$

Thus given the assumption that expenditure is proportional to the square root of disposable income, a lower GDP should be observed in economies with increased inequality of disposable income.

However if expenditure is proportional to the square of disposable income, then it can easily be shown with a similar argument that wealth distribution will increase the GDP in this simple model.

According to modern economics principles, in simple terms how does distribution of disposable income affect GDP?

Note:  I'm not interested in the politics behind equality, such as riots and revolutions etc that might result from severe inequality, but merely the financial/economic principles.

You assume that higher spending causes higher GDP. This is not necessarily true.

Saving income will increase GDP through investments (unless you're in a Keynesian trap). Think about the most standard growth model, where the future (and steady state) GDP increases strictly in the savings rate.

Hence, whatever increases the savings rate (in your toy model, higher inequality), potentially increases future GDP. This was an argument brought forward in Barro (2000). Version with linear savings rate are in Bertola et al (2006)

Of course, this is the neoclassical answer. There are many reasons why high inequality decreases growth/GDP which work through political economy models or similar. I'll list some of them despite you not asking explicitly for them, perhaps it's of use for future visitors:

Fiscal Policies: Equality leads to less necessary government redistribution and hence more incentives for investments (Meltzer and Richard, 1981; Corcoran and Evans, 2010; Persson and Tabellini, 1994)
Crime: Inequality reduces opportunity costs for illegal activities that harm GDP (Alesina and Perotti, 1993; Barro 2000)
Imperfect Credit Markets: Fixed investment costs or similar may lead to higher growth in unequal societies (Barro 2000)
Saving Incentives: With "not much to lose", poor people face moral hazard in unequal societies, a mechanism that decreases growth with inequality (Banerjee and Newman, 1991)
Fertility-Education: Equality in human capital leads to smaller fertility rates, and hence lower GDP. The Argument is too long to be summed up here; see Perotti (1996) and Croix and Doepke (2003))


Why does Fed buying mortgage backed security help lower the interest rate for mortgages? If Fed buys more mortgage-backed securities, does it necessary mean that the interest rate will go down? Consider the supply and demand market for mortgages  and let us assume the supply side is the commercial banks and the demand side is households. It follows that supply increases as Fed buys mortgage-backed securities, which causes the interest rate to fall. However, suppose that although the Fed starts buying mortgage-backed securities from the banks, the banks think it is too risky to hold mortgages after the increase in the delinquency rate during the financial crisis. So the supply does not actually increase and thus the interest rate does not increase. Is this scenario possible?

Addressing your question directly, yes, the rate will necessarily decrease. The Fed's purchases of MBS were entirely MBS that were securitized by the government-sponsored enterprises (GSEs). GSE MBS are not subject to credit risk, as the credit risk is guaranteed by the GSEs.

Further, banks underwriting mortgages that are securitized through this channel aren't holding any credit risk— the only real risk they face is that if a loan that defaults is found to have underwriting defects, the originating bank can be forced to repurchase the loan from the GSE through which it was securitized. This repurchase risk isn't trivial, but it can be controlled in a number of ways (proper underwriting being foremost among them). As a result, the channel that you posit— bank perceptions of mortgage risk increasing, offsetting rate decreases brought about by QE— is not possible.

Empirical evidence supports the above assertion— rates did in fact decrease. Krishnamurthy and Vissing-Jorgensen say:

  We document that the Federal Reserve’s purchase of long-term Treasuries and other long-term
  bonds ("QE1" in 2008-2009 and "QE2" in 2010-2011) significantly lowered nominal interest
  rates on Treasuries, Agencies, corporate bonds, and mortgage-backed securities, but with
  magnitudes that differed across bonds, across maturities, and across QE1 and QE2.


Are simultaneous equation models out of fashion? Why? According to Angrist & Pischke "Undergraduate econometrics instruction: Through our classes, darkly" (2017) Tables 2 and 3, econometrics textbooks deemphasized simultaneous equation models considerably from 1970s to 2010s. Their relative weight in terms of page count has shrunk about 4-5 times relative to other topics and they occupy very little space in modern syllabi. Edit: as 1muflon1 correctly notes, Angrist & Pischke (2017) provide evidence on undergraduate textbooks, not textbooks in general nor use of simultaneous equation models in academic or nonacademic research.
Are simultaneous equation models out of fashion (besides their coverage in undergraduate textbooks)?
If so, why did this happen?

Were these models found especially problematic?
Were alternative, more attractive models proposed to deal with the same research problems?
Were the research problems requiring structural equation models abandoned?
Did changes in data availability (size, dimensionality of samples) play a major role?
Did the increase in computational power play a major role?
Any other cause?

I think this is already answered in the paper itself and in addition I think this is due to the way how they define 'simultaneous equations' topics.
First as authors of that paper opine:

This presumably reflects
declining use of an orthodox multi-equation framework, especially in macroeconomics. The reduced coverage of Simultaneous equations has made space for modest
attention to Panel data and Causal effects, but the biggest single expansion has been
in the coverage of Functional form (mostly discrete choice and limited dependent
variable models).

In addition I would add that macroeconomic modelling also shifted more towards not just panel data but also time series as macroeconomic data with lengthy $T$ dimension became more readily avaiable.
Second, note how they define simultaneous equation models (in table 1):

Simultaneous equations models -  Discussion of multi-equation models and estimators, including identification of simultaneous equation systems and
system estimators like seemingly unrelated regressions (SUR)
and three-stage least squares (3SLS)

Third, note how they define time series [emphasis mine] (again from table 1):

Time series - Time series issues, including distributed lag models, stochastic processes, autoregressive integrated moving average
(ARIMA) modeling, vector autoregressions, and unit root
tests. This category omits narrow discussions of serial correlation as a violation of classical assumptions

Fourth, instrumental variables have their own category (again from table 1):

Instrumental variables (IV), two-stage least squares (2SLS), and
other single equation IV-estimators like limited information
maximium likelihood (LIML) and k-class estimators, the use of
IV for omitted variables and errors-in-variables problems.

As far as I can see from skimming the paper it is not possible for a topic to be in two categories at once. So in addition to the switch toward panel and time series methods note that they do not seem to define simultaneous equations the same way as they are colloquially known in economics.
For example, I often hear at conferences people colloquially referring to VAR, VECM or 2SLS as a 'simultaneous equations' model, yet authors here created separate categories for them. Yet, these models would be not included in the simultaneous equation category, and we can see that the time series category and IV category expanded relatively to the simultaneous equations category (in comparison to 1970s - table 2 column 1 vs 3).
Lastly, many simultaneous equations approaches (as defined in the paper) like 3SLS, are usually considered advanced topics. The research you cite focuses on undergraduate texts. I don't think any econometrics teacher would go over 3SLS before going over 2SLS and a reasonable writer of undergraduate econometric textbook might want to make the textbook narrow and focused as opposed to forever expanding page count as new techniques emerge. Hence, I think that often topics like 3SLS simply won't make a cut compared to more basic techniques like IV when it comes to undergraduate textbooks.
Response to questions in edit:

Were these models found especially problematic?

I don't think these models are considered generally problematic. At least no more problematic than other strands of models having separate categories in the paper you cite that are used more. I was not also able to find some work that would consider simultaneous equations approach especially problematic (although absence of evidence might not be evidence of absence). I am a graduate student and I visit conferences where I see simultaneous equations being used (although as mentioned above colloquially the term is used far more broader than how the authors of the paper define it, and of course this is just anecdotal evidence - the lowest form of evidence there is).

Were alternative, more attractive models proposed to deal with the same research problems?

Yes, many panel data/time series techniques can be considered alternative to simultaneous equations approach. For example, fixed effects regression that can correct for time invariant omitted variable bias  can be often considered more preferable to SUR.
Moreover, Angrist & Pischke in that paper subset all 'simple' simultaneous equation approaches such as 2SLS/IV in a separate category. Given that this research is about undergraduate texts I am not surprised that this kind of categorization yields this sort of result. 3SLS/Multiple eq. GMM would probably be considered too advanced for most undergraduate texts, and simple SUR is less used nowadays.

Were the research problems requiring structural equation models abandoned?

I am not aware of any such problems. As mentioned above you will see these models used in research, also typing just 3SLS Macroeconomics into google scholar with filter $year \geq 2020$ yields 262 results. Sure some of those will be just references but I would argue that this showcases that such models are still used.

Did changes in data availability (size, dimensionality of samples) play a major role?

I think yes. Currently you see a lot of research that could be done by simultaneous equations as defined by Angrist and Pischke by panel methods/time series methods.  For full disclosure I am fairly young graduate student so I don't have first hand knowledge of this transition, but I see models such as SUR being applied more in past research than in present.
Also, more generally data availability always played role in modeling choices. Even at the present you can see people more and more often talking about 'panel time series' analysis rather than just panel analysis (e.g. see ch 10.6 in Verbeek (2008) Guide to modern econometrics). The idea that people are adopting more 'time-series-esque' models is also supported by the paper you cite which shows that the focus on time series increased over time

Did the increase in computational power play a major role?

It is possible, some of the newer techniques definitely have higher computational requirements but as I am fairly young scholar I don't have much insight into how limiting factor this was when deciding between models mentioned above and I did not managed to find any relevant discussion of it (maybe someone else will provide answer).


References about leading, lagging, and coincident economic indicators Reading about macroeconomics, I've come across a great deal of information regarding leading, lagging, and coincident indicators. I'd like to learn more, specifically information related to measurements.

For the most part, in the books that I've read, indicators are graphed along side changes in the economy. I imagine that economists do not just graph items and visually inspect whether they are (1) indicators and (2) if they are leading, lagging, or coincident, etc.

What articles/texts are available that discuss the statistical methods behind identifying and measuring economic indicators?

The Wooldridge texts are part of most graduate level econometric studies in the US. They are:

Introductory Econometrics: A Modern Approach, 6th Edition
Econometric Analysis of Cross Section and Panel Data, 2nd Edition

I would start with the first, unless you have a lot of formal training in statistics.

Hope this helps,

Justin

econometrics macroeconomics statistics r

UPDATED EXAMPLES: from INTRODUCTORY ECONOMETRICS by Jeffrey M. Wooldridge

While my suggested texts about Econometrics were downvoted, below is proof of how the text book referenced illustrates how Economists can use statistical methods to identify and measure leading, lagging, and coincident relationships among economic indicators.

The first examples are from chapter 16 on Simultaneous Equations. Examples 16.4 and 16.6 illustrate how to use Instrumental-variable regression by two-stage least squares for identifying and measuring coincident economic indicators. In this case, how annual inflation drops by about 1/3 of a percent for every 1 percent increase in the import share of GDP.

The second is an example 18.8 of the text and illustrate how autoregresive $AR(1)$ and vector autoregressive $VAR(1)$ time series methods can be used to identify and measure, intertemporal relationships between economic indicators, in an attempt to forecast the future unemployment rate using today's unemployment and inflation rates.

Example 16.4: INFLATION AND OPENNESS

  "Romer (1993) proposes theoretical models of inflation that imply that more "open" countries should have lower inflation rates. His empirical analysis explains average annual inflation rates (since 1973) in terms of the average share of imports in gross domestic product since 1973 - which is his measure of openness. In addition to estimating
  the key equation by OLS, he uses instrumental variables. While Romer does not specify
  both equations in a simultaneous system, he has in mind a two-equation system:"

$$inf = \beta_{10} + \alpha_1open + \beta_{11}log(pcinc) + \mu_1$$

$$open = \beta_{20} + \alpha_2inf + \beta_{21}log(pcinc) + \beta_{22}log(land) + \mu_2$$

  "where $pcinc$ is 1980 per capita income, in U.S. dollars, assumed to be exogenous, and
  $land$ is the land area of the country in square miles, also assumed to be exogenous. The first equation is the one of interest, with the hypothesis that $\alpha < 0$. More open
  economies have lower inflation rates."

  "The second equation reflects the fact that the degree of openness might depend on the average inflation rate, as well as other factors. The variable $log(pcinc)$ appears in both equations, but $log(land)$ is assumed to appear only in the second equation.
  The idea is that, ceteris paribus, a smaller country is likely to be more open, so $\beta_{22} < 0$."

  "Using the identification rule that was stated earlier, the first equation is identified, provided $\beta_{22} \ne 0$. The second equation is $not$ identified because it contains both exogenous variables. Be we are interested in the first equation.

Example 16.6: INFLATION AND OPENNESS

  "Before we estimate the first equation in 16.4 using the data in $openness$, we check to see whether $open$ has sufficient partial correlation with the proposed $IV$, $log(land)$. The reduced form regression is:"

$$\widehat{open} = \beta_0 + \beta_{1}log(pcinc) + \beta_{2}log(land)$$

open_model <-lm(open ~ lpcinc + lland, data = openness)
summary(open_model)
Coefficients:
        Estimate Std. Error t value Pr(>|t|)

(Intercept) 117.0845    15.8483   7.388 2.97e-11

lpcinc        0.5465     1.4932   0.366    0.715
lland        -7.5671     0.8142  -9.294 1.51e-15

  "The $t$ statistic on $log(land)$ is over nine in absolute value, which verifies Romer's assertion that smaller countries are more open. The fact that $log(pcinc)$ is so insignificant in this regression is irrelevant."

  "Estimating the first equation using $log(land)$ as an $IV$ for $open$ gives:"

$$\widehat{inf} = \beta_0 + \beta_{1}open + \beta_{2}log(pcinc)$$

library(AER)
inflation_IV <- ivreg(inf ~ open + lpcinc | lpcinc + lland, data = openness)
summary(inflation_IV)

Coefficients:
        Estimate Std. Error t value Pr(>|t|)

(Intercept)  26.8993    15.4012   1.747   0.0835 .

open         -0.3375     0.1441  -2.342   0.0210
lpcinc        0.3758     2.0151   0.187   0.8524

  "The coefficient on open is statistically significant at about the 1% level against a one sided alternative of $\alpha_1 < 0$. The effect is economically important as well: for every percentage point increase in the import share of GDP, annual inflation is about 1/3 of a percentage point lower. For comparison, the OLS estimate is -0.215, $se = 0.095$."

Example 18.8: FORECASTING THE U.S. UNEMPLOYMENT RATE

  "We use the $PHILLIPS$ DATA, but only for the years 1948 through 1996, to forecast
  the U.S. civilian unemployment rate for 1997. We use two models. The first is a simple
  AR(1) model for $unem$:"

$$\widehat{unemp_t} = \beta_0 + \beta_1unem_{t-1}$$

  "In a second model, we add inflation with a lag of one year:"

$$\widehat{unemp_t} = \beta_0 + \beta_1unem_{t-1} + \beta_2inf_{t-1}$$

library(dynlm)
phillips <- ts(phillips, start = 1948)
unem_AR1 <- dynlm(unem ~ unem_1, data = phillips, end = 1996)
unem_inf_VAR1 <- dynlm(unem ~ unem_1 + inf_1, data = phillips, end = 1996)

stargazer(unem_AR1, unem_inf_VAR1, keep.stat=c("n","adj.rsq","ser")
=================================================================
                                 Dependent variable:
                    ---------------------------------------------
                                        unem
                             (1)                    (2)
-----------------------------------------------------------------
unem_1                     0.732                  0.647
                           (0.097)                (0.084)

inf_1                                             0.184
                                                  (0.041)

Constant                   1.572                  1.304
                           (0.577)                (0.490)

-----------------------------------------------------------------
Observations                  48                     48
R2                          0.554                  0.691
Adjusted R2                 0.544                  0.677
Residual Std. Error    1.049 (df = 46)        0.883 (df = 45)
F Statistic         57.132   (df = 1; 46)  50.219   (df = 2; 45)

  "The lagged inflation rate is very significant in the second model $(t \approx 4.5)$, and the adjusted R-squared much higher than that from the first. Nevertheless, this does not necessarily mean that the second equation will produce a better forecast for 1997. All we can say so far is that, using the data up through 1996, a lag of inflation helps to explain variation in the unemployment rate."

  "To obtain the forecasts for 1997, we need to know $unemployment$ and $inflation$ in 1996. These are 5.4 and 3.0, respectively. Therefore, the forecast of $unem_{1997}$ from the first equation is $1.572 + .732(5.4)$, or about $5.52$. The forecast from the second equation is $1.304 + 0.647(5.4) + 0.184(3.0)$, or about $5.35$. The actual civilian unemployment rate for 1997 was $4.9$, so both equations over-predict the actual rate. The second equation does provide a somewhat better forecast."


What is the name given to an exporting-only economy? In international trading there can be an economy that only exports, but restrict the import to minimum or at all. I cannot recall what the name given to this type of economy.

Is "autarky" the word you're looking for? That's been used to refer to a self-sufficient economy that does not need imports. As others have written, it's an ideological position that chooses to make a country worse off, and doesn't happen in reality, except for short periods in exceptional circumstances.


Does estimating OIRFs from an SVAR (with uncorrelated errors) mean we are orthogonalizing the error structure twice? In a normal VAR, we can orthogolize the errors via Cholesky decomposition and estimate OIRFs. Thus, transforming the structure of the errors so that they do not correlate.
In an SVAR, asumming we impose an orthogonal structure on the errors directly so that they do not correlate. And so if we estimate OIRFs from an SVAR instead of just IRFs, are we not orthogolizing the error structure twice? First, through a direct imposition of an uncorrelated error structure by specifying some matrix. And second, through Cholesky decomposition if we estimate OIRFs.

Usually if you are estimating a SVAR model you would use structural impulse response functions (SIRF) not orthogonalized impulse response functions.  SIRF are not exactly the same as just IRF or OIRFs. SIRF already takes the identification problem into account when the SVAR is estimated.
To learn more about this you can have look at this blog about impulse response functions in R - the blog is brief but explains difference between simple impulse response, orthogonalized impulse response, structural impulse response and generalized impulse response. More info can be found in sources cited therein.


Why don't public transport networks have more routes to each terminal in order to reduce the number of transfers? When using public transport, we often need to transfer to a different route during of the journey. These transfers have costs – I can think of the following, but there may be more.

If we're reading, working, etc. on board, we need to stop, losing flow.
We spend time moving from one route to the next.
We spend time waiting for the next departure.
There are lots of passengers moving between routes. They take up space and cause queues when on- and off-boarding. This also increases the risk of accidents.

As far as I can understand, we could decrease the necessity of transfers by having multiple routes to the terminals (final stations).

I've reviewed the subway maps of London, New York and Oslo, finding that none of them do this to any significant degree – the terminals usually have just one route leading to them.

As an example, let's look at Oslo, the simplest of the three:

Imagine that you're travelling from Østerås (orange, left) to Ellingsrudåsen (orange, right). No transfers required – route 2 takes you all the way.

But if you're travelling from Østerås to Mortensrud (purple, bottom right), you would have to transfer from route 2 to route 3 at some point.

Let's imagine that we take some of the capacity from routes 2 and 3 to serve routes 6 and 7. Route 6 (gray) serves Østerås-Mortensrud, and route 7 (brown) serves Kolsås-Ellingsrudåsen:

With this design, some transfers could be avoided. If we added enough routes, many more transfers could be avoided. Granted, we would have to wait longer for a route that went exactly to the station we want to go, but we would always have the option of taking the first train and then changing like before.

(Note that I'm not suggesting adding new infrastructure, just using the existing one differently.)

So, I've been asking myself, why don't public transport authorities do this? These are the reasons I can think of:

Simplicity for passengers: When only one route serves a particular station, you don't have to think about which route you'll board. Also, the maps are simpler with fewer routes.
Simplicity for transport providers: More routes would mean more difficult (and therefore costly) planning and management.

From my (admittedly ignorant) perspective, the above reasons don't seem sufficient to outweigh the advantages of fewer transfers.

Have I overlooked some problems with adding more routes? Or am I underestimating the costs and/or overestimating the benefits of more routes?

Note that I have previously asked this question on Engineering Stack Exchange. I'm asking it again here because I didn't get satisfactory answers, and because I think it's more of an Economics question.

The people who do the planning do explicitly account for the value of time and convenience to transit users. At least, in all the systems I've worked on. (source: I have had jobs where I worked on exactly this, for London, for the Netherlands, and for international train travel)

Interchanges are modelled with penalties which cover:

the time taken to move from one platform to the other
the time to wait for the connecting train
and a penalty to reflect the inconvenience, which is calibrated through Stated Preference surveys.

Here's an example from the Business Case Development Manual used for evaluating costs and benefits for transport schemes in Greater London, page E-10, showing a penalty just for the interchange itself of 3.5 minutes of travel time. That's in addition to valuing the waiting and walking time at the interchange:

(disclosure: I developed some of the methods, and calibrated some of the numbers, in that manual)

So, why aren't the lines designed for switching between tracks?

Well, sometimes they are: London's Circle, District and Hammersmith & City lines work very much on this basis: similar rolling stock on the same tracks, connecting different termini. London's Northern line works similarly, with two northern termini, two southern termini, and two branches in the middle, with trains switching between them.

At peak times, there is much less switching on the branches on the Northern Line. And the Circle line was recently redesigned so that it is no longer a circle.

The reasons for those two things, are the reasons why it isn't done more often.

The switching reduces the vehicle-capacity of the network. So it can move fewer passengers per hour. Which adds delays to everyone, at rush hour.

Trains are kept a minimum distance apart, in the interests of safety. Where two branches join, as they do at switch-points, that means that trains on both branches have to be kept that distance apart from trains after the switch-point. And the switch-point becomes a bottleneck, reducing the capacity of the two branches: the switch itself has a lower capacity than the sum of the capacity of the two branches.

So, during the busiest periods, there is less switching, and that minimises the overall system aggregated generalised cost. And that generalised cost includes the bullet points that I mentioned at the top of this answer. And when new lines are designed, the value of switching routes to different termini usually adds too much of a penalty to network capacity, to make it worth while: overall system utility is typically maximised with dedicated routes.

And if you don't want to do switching of vehicles on tracks, then you have to build additional grade-separated tracks to do the switching instead, and that's really really expensive, and isn't justified by the reduced generalised cost that arises from fewer passenger interchanges.


What are some historical examples of laissez-faire capitalism? I suspect both supporters and detractors of laissez-faire capitalism have some historical examples in mind that they consider reasonably close to an ideal or to a "natural experiment". (The examples used by supporters vs detractors might not actually be the same though.)

The Wikipedia article I've linked to discusses at length the history of the concept itself, but I'm having trouble finding a discussion of examples of the realization of the concept in there.

So, do these examples exist in literature? If so, what is the pro/con angle on them?

There is an ongoing project to try and estimate how close a society is to the ideal, see the indices of economic freedom by the Heritage Foundation and the Fraser Institute. The ones who made They score countries from 0 (all-pervasive state control) to 10. Hong Kong (8.91) and Singapore (8.71) are currently the two countries closest to the ideal, according to the Fraser Institute.

These projects only look at societies from 1970 and onward, however.

A few western countries during parts of the 19th Century have, in a way, been relatively close to the ideal, such as the US up till the Progressive Era and Sweden (around 1860 to 1910). However, both societies restricted the rights of married women and the southern states upheld slavery and later on Jim Crow laws against blacks. Still, the economy was mostly coordinated through private effort, though there were legal barriers for women and blacks to get into the "commanding heights" of these economies.

For a description of the decline of laissez-faire in the US, see Murray Rothbard's The Progressive Era and Robert Higgs' Crisis and Leviathan, which should complement a general economic history of the era.

Mancur Olson, an economist and critic of laissez-faire, argued in The Rise and Decline of Nations that (probably the last) 50 years of British India was among the closest to a laissez-faire ideal. During that time India had hardly started developing. He says that the caste system was a drag on the economy, but he didn't see castes as being in conflict with the freedom to enter any business or trade.

Another way to study laissez-faire systems is to look at specific markets, when not heavily regulated. The Voluntary City shows examples of the private production of urban infrastructure, roads, commercial courts, police, social insurance (healthcare, sick benefits, life insurance), and primary education in 19th century US and England. For examples of the private production of coins, see Good Money; for bank notes (and a free banking sector) see The Theory of Free Banking.

Also, for the record, I'm an ancap.


How do I derive the aggregate demand function given two utilities functions? Assume that we have two people with the same utility function of $U_i = x^{1/2} + y^{1/2}$ where $i=1,2$ and $I_i$ is the income. Let $P_x$ denote price of good $x$ and $P_y$ denote price of good $y$.

I'm being asked to derive the aggregate demand function. The only thing I got so far was finding the market demand for each good per person, which is

$x^*_1 = {I_1}/2P_x$ , $y^*_1 = {I_1}/2P_y$, for person 1

$x^*_2 = {I_2}/2P_x$, $y^*_2 = {I_2}/2P_y$ for person 2

Am I missing something? Please help.

Thanks.

If you have $J$ consumers therefore $J$ demands for a good $X$. Denoting the individual demand of each consumer with $x_j^*$ as you have it, if $X$ is the aggregate demand, it is just the sum of every individual demand:
$X=\sum_{j=1}^{J}x_j^*$
Then for your case it's: $x_1^*+x_2^*=\frac{(I_1+I_2)}{2P_X}$, and the same with $Y$.


What's the effect of the used car market on car price? While justifying his decision to sell a one-use textbook to his students, a BYU econ professor states:

  A word about the Kearl text. I do not receive any royalties and have
  no economic interest in the text – when you buy the text, not a single
  penny goes to me. It’s designed to be a “disposable,” one-use text in
  order to keep the price down. (The used book market actually drives up
  the price of texts – to see why, think of what would happen to car
  prices if used car sales were not permitted.)

How does the used car markets drive up the price of the good? With an used market, the buyer is willing to pay more, knowing that he can resell later. However, without an used market, everyone is forced to buy new cars, raising demand, and thus raising the price of new cars. How to think about these countervailing effects?
Are the used books and used cars market actually analogous like the professor suggests? The professor and his publisher have a monopoly on the new textbook, but no one controls the new car market. Therefore, if people keep reselling text, the publisher will use their monopoly and raise the price of new books to compensate. In contrast, car manufacturers can't do so due to competitive pressure.

OP,

imagine that there are two kinds of people, rich and poor, and no market for used books. Rich people are willing to pay more for new textbooks. Poor people cannot afford to buy new textbooks. In the absence of a market for used books, poor people will not buy textbooks.

Imagine now that there is market for used books. Poor people are now able and willing to pay second hand textbooks. And rich people now have someone to sell the books too once they are done using these books.

A market for second hand textbooks raises the price of textbooks because some people are not able and willing to buy new textbooks but are willing to buy second hand textbooks. In the absence of such a market, they'll spend their income on other goods that they deem more important.

Think of it otherwise this way. In the absence of a market for used textbooks the full value of a textbook is not exploited because some people who would wish to trade with one another cannot trade with one another.

Hope that this helps.


In the short run, why do higher interest rate lower inflation? A common interpretation is "Higher interest rates put less borrowing power in the hands of consumers and businesses. And when they spend less, firms are not selling everything and prices naturally falls." In the perspective of AS-AD model, this is caused by a downward shift of the demand curve.

However, I suppose it will cause a supply curve shift as well. Today, most production is funded by short-term borrowing and relied on people paying them for the goods and then pay back the loans. So when the interest rate goes up, the production costs go up, and hence causing a negative supply shock as well.

But reality seems to confirm the fact that higher interest rate lower inflation. Why is that? Is that because the supply shock is not as significant as the demand shock? If so, why is that?

In the short run, why do higher interest rate lower inflation?

As you say:

  "Higher interest rates put less borrowing power in the hands of consumers and businesses. And when they spend less, firms are not selling everything and prices naturally falls."

There's also the effect on the velocity of money and the money supply that less borrowing brings to provide further downward pressure on prices.

I also think your concerns about supply falling in the short term are unfounded. Firms are slow to adjust their production/supply due to greater borrowing costs. Instead, they will attempt to keep up production and clear their inventories by lowering prices in the hopes that the next season will show them a success and bail them out. Any such effect from interest rates would be a much longer-run effect.


How to have same utility function for two persons? I have a question regarding utility functions:

Utility can be defined as follows:

$U=1+e^{\frac{x}{RT}}$

U:Utility

x: What we want to find the utility for (Certain equivalent)

RT: Risk tolerance

My question is what conditions must be upheld in order for two persons to create the same utility functions for a bet?

Example:

Person A bet \$5 that horse number 1 wins. Person B bet \$20 that horse number 2 do not win. Is it then possible that their utility functions is the same given that their probabilities for horse 1 winning is the same and that they have the same risk tolerance?

Strictly speaking, infinitely different utility functions can give the same preferences over lotteries. If person A has utility function $f(x)$ then person B with utility function $g(x)=A+B\cdot f(x)$, $B>0$ will make identical decisions over all lotteries. There would be no way to know which individual was actually using utility function $f(x)$ and which was using $g(x)$ because they would always make the same decisions.


Questions about the Edgeworth box I have some doubts about the Edgeworth box, it's a newbie one so thanks in advance for the patience. In case, I'm confused if we can count the root of the contract curve (point M) as one of the efficient points since it's part of the contract curve? In this case the indifference curves in the box don't "touch each other", therefore is not a point of efficient trade (as if MRSa = MRSv). Is my line of thinking correct? Can we also assume that every point in the contract curve are Pareto Optimal? Is it also possible for the utility change from one to another (say from N to P)?

*MRS= Marginal Rate of substitution

...I'm confused if we can count the root of the contract curve (point M) as one of the efficient points since it's part of the contract curve?

The contract curve is, by definition, the set of all Pareto efficient allocations. Therefore, any point that you choose on this curve--even the end points--is also Pareto effecient.

  In this case the indifference curves (IC) in the box don't "touch each other", therefore is not a point of efficient trade (as if MRSa = MRSv).

In the example figure you included, they have only shown the pair of indifference curves for person A and person B at four points just to illustrate and to make the graph easy to read.  In reality, indifference curves can be found at any point (they're continuous), so there would be a pair of indifference curves that you could map on to the end point (point M) as well.

What would that look like?  From Person A's perspective, their indifference curve sits on point M and opens upwards. At this point, they have been allocated nothing, so they prefer the set of all allocations above their IC.

For Person B, their IC also sits on Point M and opens downward. Essentially, this is saying that have the entire allocation, and while they would prefer to have more, there is nothing available in the set of feasible allocations depicted in this Edgeworth Box (again, they have the entire allocation).

While this allocation may not be equitable (Person B has everything, Person A has nothing), from a Pareto perspective it is efficient in the sense that, you cannot move from this point (Point M), without making one of the people worse off--i.e. if you shift anywhere from point M, though it would make person A better off, it would make person B worse off.  So even here at the border, we can find a Pareto effecient allocation.

  Is my line of thinking correct? Can we also assume that every point in the contract curve are Pareto Optimal?

Correct.

  Is it also possible for the utility change from one to another (say from N to P)?

Not sure exactly what you mean here, but the second fundamental theorem of welfare economics says--broadly speaking--that you can get to any point on the contract curve in a competitive economy by changing the endowment allocations.


Is there any difference between using LRAC and LRATC in a long-run market structure diagram? I am currently being taught to use average total cost (ATC) to label the U-shaped curve in the short-run on a market structure diagram, and long-run average cost (LRAC) for the equivalent curve in a long-run market structure diagram.

Is any difference between labeling the U-shaped curve in a long-run market structure diagram long-run average total cost (LRATC) and labeling it LRAC?

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


How to create a Budget line in excel or R Im interested in programming a budget line or PPF which is responsive to changes in relative prices, and income effect.

I know the equation for the budget line is $$m\geq p_1x_1+p_2x_2$$
Lets say we have an Income of $m=100$ and prices $p_1=1$ and $p_2=2$.

How would I go about programming this into an excel spreadsheet or R?

Another R code that does it...

m <- 100
p <- c(1, 2)
curve(m/p[2] - p[1]/p[2]*x, from = 0, to = m/p[1], lwd = 2,
      xlab = expression(x[1]),
      ylab = expression(x[2]))


Economics perspective of connected workers According to the Deloitte report published here, a connected worker is defined as someone whose working life is changing due to digital and other technologies. This is due to advancement of technologies, such as, smart wearables, IoTs, Industry 4.0 etc, therefore, enabling the worker to work from anywhere and anytime. This implies that due to these technologies, the geographical and temporal boundaries are becoming less relevant.
Is there any perspectives that we can use from the Economics literature to understand the concept of connected workers?

You may look up the 2015 Journal of Economic Perspective paper "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" by David Autor. Look up the references therein.


What are the textbook-like obvious advantages and disadvantages of tipping? I just came from a coffee place with tipping and now I'm in one without tipping... prices are higher, but there is not tipping. It's not obvious why some places get rid of tipping and others don't. I guess you want the server to be incentivized by the possibility o tipping to give outstanding, charming service. What other considerations are there?

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

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

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

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

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

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

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

More on the following nice NPR podcasts on

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


Where can I read more about formula (G-T)= -(S-I) -(X-M)? And how is this formula called? I saw this formula in following context:

I want to create Banker State. It's supposed to rely on profit from loans instead of taxes. I would like to keep taxes as low as possible.
Reply: what you're trying to do wouldn't work unless you
were running massive trade surpluses (a la Saudi Arabia). The
government debt represents nongovernment sector equity. (G-T)= -(S-I)
-(X-M) The government fiscal deficit represents the nongovernment sector's net financial savings. Government trying to build positive
debt means forcing the nongovernment sector to go into debt simply
just to pay its tax bill. It doesn't work, it would create vertical
unemployment & the automatic fiscal stabilizers would push the
government fiscal's position (which is reaction to nongovernment
sector spending behavior) into deficit.

Since you already answered yourself I am just filling the textbook recommendation:

You could  find more on this in Blanchard et al. Macroeconomics: an European Perspective. You will find this there (assuming based on the question you are looking something at undergraduate level).


Was the aid in the Marshall Plan a loan? In the aftermath of the Second World War the USA organised the Marshall plan for the post-war reconstruction of Europe.

I take it this was actually a loan of some kind; on what kind of terms was the loan given? How long did it take a country, for example the UK or France to pay this off - or are they still paying it off? How much was the loan worth in real terms if the interest due on the loan was discounted?

The plan did involve loans but was mostly in the form of grants (gifts). As the Wikipedia entry states:

  The Marshall Plan [...] consisted of aid both in the form of grants and in the form of loans. Out of the total, 1.2 billion USD were loan-aid.

Since the total economic support the program involved was of \$13 billion in contemporaneous time (roughly \$132 billion in Sept 2017 money), only 10% approx of the aid was in form of loans.

  The UK received 385 million USD of its Marshall Plan aid in the form of loans. Unconnected to the Marshall Plan the UK also received direct loans from the US amounting to 4.6 billion USD. The proportion of Marshall Plan loans versus Marshall Plan grants was roughly 15% to 85% for both the UK and France.

The last payment of these loans by the UK government was done in 2006. Apparently, the last payment from the French occured in 1962.

  Germany, which up until the 1953 Debt agreement had to work on the assumption that all the Marshall Plan aid was to be repaid, spent its funds very carefully. Payment for Marshall Plan goods, "counterpart funds", were administered by the Reconstruction Credit Institute, which used the funds for loans inside Germany. In the 1953 Debt agreement the amount of Marshall plan aid that Germany was to repay was reduced to less than 1 billion USD. This made the proportion of loans versus grants to Germany similar to that of France and the UK. The final German loan repayment was made in 1971.


Conditional variance vs. unconditional variance in ARCH model I am in the process of working through some problem sets. I have studied some time series, but my knowledge of ARCH models is pretty basic. I am given the following information:

$Y_t = a_0 + a_1 Y_{t-1} + \epsilon_t$

where $\epsilon_t | I_{t-1} \sim N(0,h_t)$ and $h_t = \alpha_0 + \alpha_1 \epsilon_{t-1}^2 + \alpha_2  \epsilon_{t-2}^2$

I solved for the following,

Conditional variance:

$E(Y_t | I_{t-1}) = a_0 + a_1 Y_{t-1}= \mu$

then

$Var(Y_t)=E[(Y_t- \mu)^2] = E(E_t^2) = ht = \alpha_0 + \alpha_1 \epsilon_{t-1}^2 + \alpha_2  \epsilon_{t-2}^2$

Unconditional Variance:

At this point I think we can create a new series for $Y_t$ since we are not conditioning, so I wrote,

$Y_t = a_0 + a_1 (a_0 + a_1 Y_{t-2} + \epsilon_{t-1}) + \epsilon_t$, repeat this infinitely many times and get

$Y_t = \frac{a_0}{1-a_1} + \sum_{j=0}^\infty a_1^j \epsilon_{t-j}$

$E(Y_t) = \frac{a_0}{1-a_1}$ and

$Var(Y_t) = E[(Y_t - \mu )^2] = \sum_{j=0}^\infty a_1^{2j} \epsilon_{t-j}^2 = (\alpha_0 + \alpha_1 \epsilon_{t-1}^2 + \alpha_2  \epsilon_{t-2}^2) + a_1^2 (\alpha_0 + \alpha_1 \epsilon_{t-2}^2 + \alpha_2  \epsilon_{t-3}^2) + a_1^4(\alpha_0 + \alpha_1 \epsilon_{t-3}^2 + \alpha_2  \epsilon_{t-4}^2) + \dots$

I am not sure if this cleans up nicely, but what does this say for the variance? it seems like the unconditional is just going to blow up infinitely (given I could have made a mistake in the derivation of this question). I am also not sure if I should state some stability conditions in this question similar to AR(1) processes (e.g., $\alpha_1 < 1$ and $\alpha_2 < 1$), since these are variances do those conditions apply?

If I was to conclude a statement, is it acceptable to stay the model captures volatility clustering via 2 period lags?

I cannot directly answer your question, but I think I can shed some light. What I seem to show is that under some restrictions, the unconditional variance is finite. However, I am not sure how to relate my answer to the notion of volatility clustering.

Note that if we assume stationarity, then $\sigma^2=E(\epsilon_t^2)$ for all $t$ and it follows by the law of total expectations that $$\sigma^2=E(E(\epsilon_t^2|I_{t-1}))=E(h_t)=\alpha_0+\alpha_1\sigma^2+\alpha_2\sigma^2$$ using your expression for $h_t$. Thus, $\sigma^2=\frac{\alpha_0}{1-(\alpha_1+\alpha_2)}$ is a solution whenever $\alpha_1+\alpha_2<1$.

Now, by the law of total variance, \begin{align*}V(Y_t)&=E(V(Y_t|I_{t-1}))+V(E(Y_t|I_{t-1}))\\
&=E(\epsilon_t^2)+V(\mu_t)\\
&=\sigma^2+a_1^2V(Y_{t-1}) \end{align*} using your results, where $\mu_t=a_0+a_1 Y_{t-1}$. Assuming stationarity, and thus that the variance is constant for all $t$, it follows that $$V(Y_t)=\frac{\sigma^2}{1-a_1^2}$$ is a solution for $a_1\neq\pm 1$ and $\alpha_1+\alpha_2<1$.

I encourage you to clearly note my assumptions. I cannot further assess this answer as I know next to nothing about time series.

(Also note that we from the assumption of stationarity have that $E(Y_t)=a_0+a_1E(Y_{t-1})$ implies that $$E(Y_t)=\frac{a_0}{1-a_1}$$ is a solution.)


What is "Observed realized measure"? I am working with a Realized GARCH model. I have am having some trouble understanding a concept:

Paper: Realized GARCH: A Complete Model of Returns and Realized Measures of Volatility, Hansen & Haung

  "A key feature of the Realized GARCH framework is a measurement
  equation that relates the observed realized measure to latent
  volatility".

Or from the rugarch-vignette (R), page 14:

  "Unlike the naive augmentation of GARCH processes by a realized
  measures, the realGARCH model relates the observed realized
  measure to the latent volatility..."

I am confused to exactly does "observed realized measure" mean?

I presume, that you are familiar with the subject and need clarification only upon the expression in question.

I also preume that you know what volatility is. Volatility is generally unobserved in GARCH models, i.e. it can not be measured directly in which sence it is latent volatility. Usual GARCH model derives an estimate for this latent volatility from squared errors.

In this paper the way to incorporate a different estimatior of the latent volatility is sujested. As it is not entirely accurate to call it an estimator, that refer to it as measure. They consider measures from the family that is commonly referred to as realized measures of volatility, e.g. realized varianse and bipower variance. So we have realized measure to latent volatility. However we can not observe a random variable, which this realized measure actually. Instead we observe one realization of it - an observation. So the numbers that we got from using these procedure are called "observed realized measure to latent variation".

If my explanations were unclear or something else needs to be explained please specify it.


Is craigslist part of the Shadow Economy? Is craigslist part of the Shadow Economy? Many if not most of the purchases that take place there are done without taxation.

If we take the definition of shadow economy to be illicit economic activity existing alongside the traditional economy and further pin down illicit to mean any sort of economic activity that violates laws governing our conduct (civil, economic etc.) then the short answer is - yes.

Why? Many reasons. I think the easiest to verify and perhaps most obvious is that the majority of gigs, services, temporary labor positions, etc. on Craigslist are paid in cash. Here I make an assumption: most of the people doing these jobs for cash are not reporting these extra earnings. Thus, they are generating a supplemental income that goes untaxed.

I read a bit of (non-academic) research about how these sorts of sites often provide a lifeline to illegal immigrants precisely because they facilitate economic illicit activity (I will update this post with the link if I can dig it up).

Why else? Again I speculate (but reasonably). Craigslist, I think, is often used to move stolen property. Because transactions are largely anonymous and conducted on a cash basis and with little or no record of sale, CL is a safe place for petty criminals to liquidate stolen assets. This also clearly aligns with our given definition.

You could probably think of other reasons to support the claim that CL fuels (alternatively: is part of) the shadow economy in America. Then again, I think these two examples make it clear (if you are willing to accept the assumptions throughout the post).


Can a position at a hedge fund be securitized? Let's say that I'm an investor in a hedge fund, a recession just hit and I need cash asap. But the hedge fund either has infrequent redemption, or it locks my fund in, until the market gets better and they can liquidate the assets at a better price.

If I need money, can I securitize my position at the fund? So i can either sell that as an asset at a discount or use it as a collateral.

Actual securitization would be impractical for a number of reasons, but secondary market transactions of shares in hedge funds do occur with the consent of the funds in question (though it’s not a particularly liquid market). Using these shares as collateral would not generally be practical, as they’re not assignable interests.


Financial Smart Contract on Ethereum: can an Ether Futures contract be priced in Ether? BACKGROUND

This is more in the wheel-house of economics than programming, but after a few different titles, I thought it best not to skirt around the root of my question OR create a oddly phrased USD based question; when I'm actually asking about Ether. Just briefly, Ethereum is a blockchain network and Ether is the main crypto-currency on it that is used to pay the miners / validators, yada, yada.

Other tokens exist and it's possible to program an arbitrary contract like a financial derivative, but it doesn't have (and cannot have) a tokenized version of a US dollar without someone being able to cheat you if you ask for delivery or lie to you if you just ask for a price-feed.  Basically, just like the real world, there's counter-party risk when you have to ask a human to help you or give you information. So the hard part here is getting an accurate picture of the current purchasing power of 1 Ether without simply asking a human to provide you with the current USD:ETH price feed.

QUESTION

I'm a programmer, not an economist, but crypto-currencies are just ridiculously volatile. I would like to create asset market on Ethereum to help stabilize the value of Ether over the long term. I was thinking that perhaps I could create a contract in the form of "Pay X Ether now to have the option to buy Y Ether in 90 days". Except when I start to walk myself through an example, it doesn't make sense. I think I've structured it wrong and it seems like one side is guaranteed to lose and the other guaranteed to win regardless of whether the objective purchasing power of Ether goes up or down.

Phrased differently, using USD as a more familiar asset, I want to glean information about the current value (purchasing power) of \$1 USD today by creating a derivative of \$1 USD deliverable at some point in the future... without access to anyone's opinion of what the price is other than the current and historical order-book / trades between people of the real asset and the derivative.

The other caveat here is to avoid delivery 'now' for a 'promise' in the future, as that is wide open to theft and 99.99% of participants on Ethereum are anonymous, so no person can be trusted (only the code is trusted to execute as-written).

If I'm way off base with how I've thought the Asset:Derivative pair should work, please provide further guidance or alternatives. Especially real-world examples I can research.

FOLLOWUP

Later, the intent is that a Market Maker contract will trade both sides of the asset (Ether Vs Ether's long term sentiment) to guarantee high-liquidity and easy price-discovery. The MM assumes only that the further the price is away from the long term mean, the more it should offer to trade at the current spot price, to buy back later when sentiment change back to the other side again. This, I believe will reduce volatility (as measured in purchasing power) given enough liquidity (some double digit % of total market cap locked in it I think would be quite influential).

The MM would:

Skim profits from traders during high volatility.
Neither win nor lose during stable periods as volume it offered would be negligible compared to the total assets it held.
Be susceptible to losses as long term trend could cause the MM to be over committed to one side of the market than the other, expecting a return to mean that never comes.

Over-sell Ether in a long term bull market and lose out on gains.
Over-buy Ether in a long term bear market and lose objective purchasing power.

Futures and Options are all about exchanging something for something else. These kind of derivatives all involve exchanging different 'kinds' of goods so it doesn't make sense to have both sides denominated in the same currency: When you have X Ethereum which you are willing to give up for Y Ethereum later, what you have is a loan. Loans aren't going to do much in terms of managing volatility, which is essentially, how much is Ethereum worth in terms of other stuff.

So the futures contracts needs to be exchanging Ethereum for that other stuff that happens to be volatile relative to Ethereum or vice versa. (could be dollars, could be Bitcoins). Of course since we can't predict the future there will be a winner and a loser. It's the nature of this sort of thing, however supposedly both parties are gaining a loss of volatility.

If you want to manage the downside, you can structure it as an option. An example of this would be, You pay \$1 now to have the right to exchange \$300 for 1 Ethereum in 90 days. (or vice versa). If the Ethereum ends up being worth more than \$301 then you end up winning on that deal, and if Ethereum ends up being worth less than \$301, you don't buy it. This benefits both parties: The person selling the option gets \$1 either way, and if Ethereum drops the purchaser of the option doesnt't lose more than \$1. This type of option is called a 'call option'. If structured the opposite way (you pay \$1 for the right to exchange 1 Ethereum for \$300 in 90 days) then the opposite happens: you 'win' if Ethereum drops below \$299, and you don't exercise the option if Ethereum rises in value, in which case you're simply out \$1. This is called a 'put option'.

No matter what, there will be a winner and a loser come the time to exercise the option. This is inevitable. However, the big idea is that the buyer of the option has a certain expectation on the market trend but wants to protect themselves against the opposite trend (in the case of call, fixed loss and unlimited upside, in the case of a put, guaranteed gain if there is downward movement vs. fixed loss if there is upward movement). The opposite side, the side selling the option, makes money off of the fee to purchase the option and is hoping that the volatility works out to less than the cost of the fee.

You are right about fixed futures (where the price and quantity are fixed and there is no option to back out): they will definitely have a winner and a loser. However, both parties get a guaranteed price, and that may be more valuable than any potential gains or losses. For example, say I am a multinational corporation where most of my profits come in USD but have just built a factory in Kenya and have taken out a loan to do so from a local bank. To pay off the loan, I need to guarantee I can get a fixed amount of Kenyan shilling every month, and it may be worth it to set price in USD so I don't have to worry about exchange fluctuations causing the loan to cost more USD than I expect one month, leaving me short; in that case I won't care too much if I lose out on the potential for the USD/Kenyan Shilling rate to move in my favour. Maybe the same thing could apply for someone who has contracts denominated in Ethereum, who knows.

On the other hand, any kind of investment based on expected future trends is just a smarter form of gambling, when you find someone to take the other side of the bet of course someone will win and someone will lose.

Enforceability of such contracts is a separate measure, it would be the same problem for any futures market and ideally that is what contracts, reputation and the rule of law is for.


What properties must a utility function have such that we can define level sets and thus indifference curves? I asked on Math SE a bit about level sets here.

Based on what I learned, it seems like we usually assume a level set $L(f)$ and its function $f$ defined on $\Bbb{R}^n$ have the following properties:

$f$ is continuous
Thus, the curves in the level set are closed curves. (not sure why)
All closed curves are "converging" to a point. (not sure why)
This point will be a maximum for the function: the derivative in the point will be zero, since if it was not then the point would be a 1-manifold, which is absurd.
$\nabla f \neq 0$ on the rest of the points

I don't understand how we know a preference relation on a consumption bundle has a utility function representation such that these properties hold. According to my professor, we use level sets to describe indifference curves.

Therefore, if indifference curves are described by a level set, that level set must have the properties listed above. Note, for economics, we are considering a function $U(\mathbf{x})$, where $\mathbf{x}=(x_1,...,x_n)$, and the level set $L(U)$. Also, note $U: \Bbb{R}^n\rightarrow \Bbb{R}$.

My Question:

In economic terms, what assumption do we have to make to ensure, $U$, $L(U)$ have these properties to create a level set used for indifference curves?

Ideally, it would be great to have an enumerated list of the assumptions (ie 1,2,3).

Level sets are always well defined. No property of utility functions has to be assumed. For any utility level $\bar u$ just define the level set to be $\{x\in \mathbb{R}^n|U(x)=\bar u\}$. No property of $U$ must be assumed. This is well-defined regardless of the properties of $U$. Properties of $U$ are useful if one wants to derive specific properties of the level sets, but are not needed to define them.


Manipulating index numbers I have a producer price index which is the composite of 9 various commodity prices including that of coal. I want to remove the effects of coal price changes from the original index. I have data on coal prices. Does anyone know of a method that I could use to filter out the effects of coal price changes from the original index?

The best way is taking the index "ingredients" and rebuild it... assuming you manage the index data on a DB or on an excel file, that should not be too difficult

Otherwise, assuming you're index ingredients are averaged equally, then in a sense, the index is calculated as:

Index = (1 + Product1ChangeIn%)^(1/9) * (1 + Product2ChangeIn%)^(1/9) ...

So based on it, without coal, you're index should be:

Index / (1 + CoalChangeIn%)^(1/9)


Business plan template i have to do a business plan as an assignment in my computer science engineering school. I'm looking for help .. can anyone provides me with a complete template ? or an example to follow

thanks,

There are many online. I don't recommend any of them, unless you're looking for a structure to guide you through. Business plans must suit the idea, and not otherwise.

You can find generic templates out there, but I believe they should work as a guidelines - i.e., things you should include. But the point of a business plan is to explain everything about an idea/project and explore the outcomes from there. If you are able to explain every single detail of your idea, you'll find some weaknesses - which you must include, of course - and your exercise is now to think about how can you over come those weaknesses.

Some questions for you to consider:

Who are you? Why are you proceeding with this idea?
What is the idea? What is the product? Why is it so different?
Where are you/your firm located? Why? Any particular reason? Where should it be located if you could?
What is your market? Age, type, profession, gender, etc etc
How do you expect to reach out to those people about the idea? After that, how do you want them to buy your product/service?
(At this point, you can perform the so-well-known SWOT analysis and other similar matrices (BCG matrix, for instance).
How are you going to finance yourself? Why? Why or why not loans, business angels, credit, etc etc ? What is your financial strategy? How are you going to manage the (few) cash you will have?
Are you going to invest on marketing? If so, how, why, why not?
Okay, you go some sales. What are you going to do with the cash you just earned? Why? For how long are you keeping that strategy
How are things going to unfold once you get it running? Why? Why not in a totally different way?

There are no identical business plans. You have to adequate yours to your idea. A quick example: When there is a physical product, you must include the design, explain it, whys and why not. When there is a service, for instance, perhaps it is better to explain why is it different than other solutions (you know, reinventing the wheel, not inventing it).

I hope these will help you work something around.


is "output" another way of saying "real gdp"? I keep hearing "output" alot and even "real output" sometimes, do these terms has to do with Nominal or Real GDP" ? because i a confused and could not really find anything useful on the internet
Thanks in advance

In the UN System of National Accounts (SNA 2008),
$$
\begin{aligned}
\text{GDP}       &= \text{Total Value-Added}, \\
\text{Output}    &= \text{Total Production}.
\end{aligned}
$$
Since $\text{Value-Added} = \text{Production} - \text{Intermediate Consumption (IC)}$, we have $$\text{GDP} = \text{Output} - \text{Total IC}.$$
(IC refers to goods completely consumed as inputs during production.)

Notes:

As already noted in the other two answers, in practice, economists, journalists, and other writers often simply use the term output as a synonym for GDP, even though this is strictly speaking wrong (or at least inconsistent with SNA usage).

As stated in "National Accounts: A Practical Introduction" (UN, 2003):

GDP is sometimes called in economic textbooks "output" or "net output". However, output has a different meaning in national accounts.

Contrary to what the other two answers may have suggested, the difference between GDP and output has nothing to do with home production. Instead and as shown above, the only difference between the two is IC.

The term real in economics (usually) refers to whether the figures have been adjusted for inflation. This has nothing to do with the difference between GDP and output. For any year, we have

$$
\begin{aligned}
\text{Real GDP} &= \text{Real Output} - \text{Real Total IC}, \\
\text{Nominal GDP} &= \text{Nominal Output} - \text{Nominal Total IC}.
\end{aligned}
$$

The term net (contrasted with gross) in SNA refers to whether (or not) depreciation has been accounted for. So, $$\text{Net Domestic Product (NDP)} = \text{GDP} - \text{Depreciation}.$$

It would be somewhat strange to speak of net output (this would mean we've accounted for depreciation but not IC). And indeed SNA makes no use of this term.
But again, as in Note 1, net output is sometimes loosely (and wrongly) used as a synonym for GDP. But this is especially mistaken and confusing because such use of net seems to refer to whether IC has been accounted for, whereas in the SNA, net refers to whether depreciation (rather than IC) has been accounted for.


Making predictions with a distributed lag model I have a question regarding making predictions from distributed lag models. Suppose, I have a simple linear distributed lag model of the form:

$$y\left(t\right)= \sum^{k}_{i=0}\beta_{i}x\left(t-i\right) + \epsilon\left(t\right)$$

Assuming co-linearity is not an issue. Suppose, I have many data points along the temporal and spatial domain for the explanatory variable $x$. However, I only have data for one time instance for the response variable but I have many data points in a spatial domain. Assume that there is homogeneity in the spatial domain.

The variables have a spatial domain; however, I am ignoring this and assuming homogeneity int the spatial domain for both $x$ and $y$. Also, many values of $y$ can correspond to a single value of $x$.

Is it possible to use the model for prediction? In the sense that one could take the lag coefficients and say at the initial time the only effect of the explanatory variable on the response variable is $\beta_{o}$ and at some time in the future, $t=n$, the only effect of the explanatory variable on the response variable is $\beta_{n}$.

Thanks in advance for you time and consideration.

So the short answer is yes, you can generate some predictions based on your model. Whether it will generate good predictions is another question. Basically you are only using the spatial variation to estimate your model and no time variation. So your predictions would most likely be very poor unless the relationship is very stable over time. There's also no way of knowing which lagged values of the explanatory variable would be useful in prediction, unless there is some spatial structure that you can use (i.e. some regions are leading indicators for other regions)


Why is the Total Current Account Balance of World positive and does it mean that the world has a net Trade Surplus? I wondered if it was possible for all countries to have a Trade Surplus - which turns out to be impossible because the total value of imports in the world must be equal to the total value of exports in the world - because one country's export is another's import, and vice-vera. With this information, I can make 2 assumptions:

All countries cannot have a trade surplus simultaneously.
The total trade surplus in the world (sum of C.A.B. of all countries with positive C.A.B.) must be equal to the total deficit. Inversely, the sum of C.A.B. of all countries must be zero.

I looked at the data for the Current Account Balance (2017 estimate) for all countries, from CIA World Factbook. However, contradictory to my assumption, here's what I observed:

Total Surplus (excluding EU from the list) = $1,469,654,370,000
Total Deficit = -$1,103,897,200,000
Net Balance = Surplus + Deficit = $365,757,170,000

A positive net balance should point out that the world has a net trade surplus - which I wouldn't expect. This can arise because of incorrect assumptions, flawed data or errors in calculation.

My question is about why I make this observation, or if my assumption is wrong, why and what does the data actually mean?

Since we are not trading with aliens (yet), you are right in assuming the global account balance ought to be zero. Your data contradicts this because it is collected by numerous statistical agencies who differ in their capability and their methods. Examples for different outcomes could arise from:

different degrees of border control. The fact that an export is registered in country A does not mean it is registered as an import in country B.
different capabilities in processing and collecting data. In some development countries, a substantial share of the economy is informal, i.e. outside the scope of any authority.
different statistical concepts applied.

There are datasets which try to overcome these difficulties by correcting them such that the global account balance is zero.


Marginal Effects Interpretation I'm reading a paper that tries to estimate the following model:
$$y = \alpha x+\beta z+\gamma xz+\epsilon$$
where x,z are dummy variables and y isn't.
The estimated coefficient of $\alpha$ is positive (0.02), and the sum of $\alpha+\gamma$ is negative (-0.01,insignificant). The writer examines the marginal effect of x, conditional on z and finds a positive (0.01, with significant of 90%).
My question is what is the difference in interpretation of marginal effect VS. $\alpha+\gamma$?

The paper is "Terrorism and Voting: The Effect of Rocket Threat on Voting in Israeli Elections" by Anna Getmansky and Thomas Zeitzoff (link: https://www.zeitzoff.com/uploads/2/2/4/1/22413724/zeitzoff_getmank_rockets_main.pdf).
The marginal analysis is on page 11.

The paper computes the value of your coefficient $\gamma$ on its own with comparison to $\alpha +\gamma$1.

The interpretation in this context, would be that there is a limit of the returns to RightShare given the presence of observations existing contemporaneously with RightPM and InRange.

1. this of course assumes that $z=1$


Why does demand curve shift in, in a monopolistic competitive market? In a monopolistic competitive market, demand curves shift in, if more firms enter the market. I can't seem to wrap my head around it. Demand remains the same, supply has increased. Shouldn't entry of more firms change the supply curve, rather than demand curve?

Demand for existing firms' product shifts in because the entering firms attract some of the users.


Is a home (primary residence) purchase a form of investment in macroeconomics? From Wikipedia, investment in macroeconomics is the purchase of goods that are not consumed within a short amount of time (say 3 years). In this sense, home purchase seems to be a form of investment. However, as per my understanding, investment should also involve purchasing new capital goods, which seems to me that primary residence does not qualify. I'm quite confused here: do we consider homes which are only used by their owners as investment, even if these homes are newly built?

Home construction is a form of investment, home purchase is not. There's an important macro difference between creating the capital asset (construction) and transferring it (purchase).

Regarding whether a newly built home occupied by its owner counts as investment— yes, because it is a capital asset that produces a stream of housing services. There's more detail on how this fits into GDP in this answer.


IV：Quasi-random variance V.S. 'Placebo' check I read some papers about transportation infrastructure. I am confuse with the following two case.

Some routes are unlikely to have been randomly assigned across cities over time. Thus, In some papers(i.e. Baum-Snow, 2007), they attain consistent estimates of the effect of transportation network on interest by the planned route IV.
But some papers (i.e. Donaldson 2012) use the planned route IV to test 'Placebo' effect.

These two case seems contradictory. I'm quite new to this and hope someone can help me.

There is no contradiction in two approaches.
Baum-Snow (2007) looks at the effect of the Interstate Highway construction on those MSAs which happened to lie on its way. The randomness comes from the fact that when the plan for the Interstate Highway project was considered, the aim was to connect distant areas. Through which MSAs the highway would go was more or less random (at least this is what the author claims). Thus, some MSAs received treatment by lying on the way whereas other didn't.

Donaldson (2012) uses planned railway routes that were NOT implemented for a placebo test. The concern which Donaldson addresses is that there could exist some unobservable economic factors (different economic development, agricultural income etc) affecting the decision about where to construct the railway. To overcome this, the key assumption he uses is that, on the stage of planning, the several proposed routes were equally desirable from the strategic point of few, but some of them were more costly to build, hence these routes were dropped at some stages of construction.

Now, if we saw an effect on income in areas where railroads were planned but never built compared to areas where railroads were not even planned, this would suggest that railways are probably not the reason to explain main findings in the paper, but rather the selection of more developed areas into treatment (receiving or at least planning to receive railroads) was in place. However, such placebo test shows that areas that were initially selected into construction of railway, but due to technical reasons didn't receive the railways - have no significant income effect. Hence, the selection is argued to be not an issue in Donaldson's identification.


Why do farmers need short hedge? I can't appreciate the red phrase. Pls see the question in the title. Why can't farmers simply sell their wheat ahead of time to buyers who are capable of storing this wheat, rather than inducing speculators to long the farmers' short positions? Why do farmers need $\color{red}{\text{short hedge}}$?
Zvi Bodie, Alex Kane, Alan J. Marcus. Investments (2018 11 edn). pp. 768-769.

Normal Backwardation

This theory is associated with the famous British economists John Maynard Keynes and
John Hicks. They argued that for most commodities there are natural hedgers who wish
to shed risk. For example, wheat
farmers desire to shed the risk
of uncertain wheat prices. These
farmers will take short positions to
deliver wheat at a guaranteed price;
$\color{red}{\text{they will short hedge. To induce
speculators to take the corresponding
long positions}}$, the farmers
need to offer them an expectation
of profit. They will enter the long
side of the contract only if the
futures price is below the expected
spot price of wheat, for an expected
profit of $E(P_T) − F_0$. The speculators’
expected profit is the farmers’
expected loss, but farmers are
willing to bear this expected loss to
avoid the risk of uncertain wheat
prices. The theory of normal backwardation
thus suggests that the
futures price will be bid down to a level below the expected spot price and will rise over the life of the contract until the maturity
date, at which point $F_T = P_T$.

"Why can't farmers simply sell their wheat ahead of time to buyers who are capable of storing this wheat..."

"...sell their wheat ahead of time..." is precisely what a futures contract does.

To imply that it is necessarily speculators on the other side is misleading.
On a futures exchange, trading is anonymous and identity of counterparty is not known. The counterparty can be a hedge fund taking a long speculative position or a cereal company who intends to take physical delivery.

For the short hedger (farmers in your example), it doesn't matter who is on the other side of the hedge. The futures position eliminates price risk regardless. It's a risk sharing arrangement. The party which has more incentive to shed risk must induce the other side to take the trade. If the short side is more risk averse, this leads to backwardation. Otherwise, contango.

(In particular, empirically expectation hypothesis does not hold for physical commodities. For financial products, no arbitrage would tell you EH holds not under the physical measure but the risk neutral measure.)


Utility and the tyranny of a minority Follow on to this question.

Using similar constraints:

  The constraints regarding the system I'm modeling are (and looking for a general solution to):

  Marginal Utility for every unit of a good must be positive (or zero), finite, and declining (though never to below zero).
  The available quantities of all goods must be finite, though they can be arbitrarily large.
  2.a There can be an arbitrarily large, though finite number of other goods in the system.
  Aggregate Utility must increase for all, while individual utility must decrease for all except one (the "monster") when a good of a certain class (let's say "cars") is transferred from any member of the group to the "monster".
  Condition 3 should be met for all transfers of "cars" from "innocents" (people not the monster) to the "monster", to the exhaustion of "cars" from the system.

The Difference

Let's replace the "monster" with a group of individuals, still in the minority.

Can a mathematical relationship be defined between their utility function over a good (x), and their representation in a population (p), that sets lower bounds for the existence of a class of "utility monsters".

For example, if members of the "monster" group a have function $u(x)=Ax+\frac{b}{x^2}$ and members of the innocent (i.e. non-monster group) have utility function $v(x)=Cx+\frac{d}{x^2}$, can we describe the ratio of $M:I$ (where $M$ is the size of the "monster" group and $I$ is the size of the "innocent" group) that allows $M$ to exist as a "utility monster" class solely in terms of $A$, $b$, $C$, and $d$?

This is basically a question in regards to constructing non-Pareto economies in simulation.

Solution:

If we assume the Group Utility function equals:

$$G(x)= M\times u(qx) + I \times v(rx)$$

where:

$$u(x) = Ax+\frac bx$$

$$v(x) = Cx+\frac dx$$

and:

$$q+r=1$$
$$q,r,A,b,C,D \in\mathbb{R}(0,\infty)$$
$$x,M,I\in\mathbb{Z} [1,\infty)$$

We can state our goal as defining the boundaries of a situation where for some unfair allocation $M<I$ and $q>r$, $G(x)$ is greater than it would be if $r>q$ (a more "fair" allocation).

We can even simplify the question to finding a set of constraints on the variables $M,I,q,r,A,b,C,$ and $d$ such that would satisfy the inequality (which assumes $q$ and $r$ are equal and dropped):

$$M(Ax + \frac {b}{x}) > I(Cx+\frac{d}{x})$$

$$x\in \mathbb{Z}[1,\infty)$$

so taking the $\lim_{x\to\infty}$ of both sides, we can see that if
$\frac{A}{C}>\frac{I}{M}$ at large $x$ the inequality is satisfied.

Conversely, at $x=1$, we get $M(A+b)$ and $I(C+d)$.

This would demonstrate that a non-Pareto optimal solution exists that still satisfies the idea of declining marginal utility and maximizes overall group utility for all distributions of goods for any class of goods where there are two preference functions $u(x)$ and $v(x)$ as long as they are distributed such that[1]:

$$\infty>AM>CI+d>0$$

Or in general a "monster class" can exist as long as the coefficients of its utility function are greater than that of an innocent class times it's fraction of the population times the total number of goods the class under consideration in the economy:

$$\{c_M|c_M>c_I\times\frac{I}{M}\times x\}$$

[1] Again, assuming that $MU \geq0$ is always true


Proximity-concentration hypothesis: homogeneous firms and symmetric countries Hi everyone I am running into a stone wall and don't seem to be able to solve this, can someone show me the steps how I can insert 1 into 2 and get 3.

$B=\frac{f_E+f_D}{φ^{(σ−1)}*(1+τ^{(1-σ)})}$

$π = φ^{(σ−1)}*B+φ^{(σ−1)}B-f_E-2f_D$

$\frac{2f_D}{f_E} = τ^{(1-σ)} -1$

I am trying to understand how the symmetric model of the proximity-concentration is derived. Would love to show you guys my steps but to be honest I haven't gotten much further than
$π = φ^{(σ−1)}*\frac{f_E+f_D}{φ^{(σ−1)}*(1+τ^{(1-σ)})}+φ^{(σ−1)}\frac{f_E+f_D}{φ^{(σ−1)}*(1+τ^{(1-σ)})}-f_E-2f_D$
and then setting $π = 0$. The paper sets $π<0$ but for the purpose of question and my issues setting $π = 0$ difficult enough.
Appreciate any and all help.


How penalties on repo fails work? There are a couple of questions I have about imposing penalties on repo fails.

Is the repo fail penalty imposed only on the seller of securities if
it does not buy the securities back or also on the buyer if it does
not return the collateral?
I read that a lack of penalties sets the repo floor at 0%, I don't get why this is so.

In the US, a fail penalty is applied on the failure to deliver securities in a US Treasury or Agency transaction.  This applies to any trade in these securities , including a simple cash trade , or the opening leg of a repo, or the closing leg of a repo.
The intent of the system , first introduced in 2012, was to clean up fails in the Marketplace by providing an incentive to settle.  The convention in the marketplace is that if there is a settlement failure, then further daily attempts to settle are made until the trade clears.  When this happens , the cash amount instructed doesn’t change over time.  Therefore, a counterparty that fails to deliver securities is effectively making a decision to lend money at zero interest.  But in the absence of a fails penalty, this may be logical if interest rates are close to zero and/or the securities are scarce.
In addition, In the absence of a fails penalty, repo cannot trade below zero.  Why?  There’s no point in lending money via repo at a negative rate in order to borrow securities that you have sold short.  It is better economically just to fail on the cash trade.


What interesting insights can be gleaned from specific data on imports and exports. There is a ton of open data available on how much of a particular good is imported/exported.  So, let's say I have 20 years of data on how many tomatoes are imported to each country in Scandinavia? Who might care, and what theoretical insights on international trade are supported by such data?

Studies in international trade are already using trade data at a particularly disaggregated level, at the transaction level.

For instance, this very new paper on Global Firms points out that research in international trade has changed dramatically over the last twenty years, as attention has shifted from countries and industries towards the firms actually engaged in international trade. They outline a framework that allows firms to be of positive measure and to decide simultaneously on the set of production locations, export markets, input sources, products to export, and inputs to import. They use this framework to interpret features of U.S. firm and trade transactions data and highlight interdependencies across these margins of firm international participation.

However, their data is from the U.S. Customs Bureau data and quite confidential. Can you give the source of "ton of open data available on on how much of a particular good is imported/exported"?


A question about MWG Exercise 3.D.4 I'm doing exercises of Chapter3 of MWG, there's a problem that I don't understand (I didn't figure out the solution manual either...).

It is about exercise 3.D.4, the full statement of the exercise is as follows:

Let $(-\infty,\infty)\times R_+^{L-1}$ denote the consumption set, and assume that preferences are strictly convex and quasilinear. Normalize $p_1=1$.

(a) Show that the Walrasian demand functions for goods $2,...,L$ are independent of wealth. What does this imply about the wealth effect of demand for good 1?

(b) Argue that the indirect utility function can be written in the form $v(p,w)=w+\Phi(p)$ for some function $\Phi(·)$.

(c) Suppose, for simplicity, that $L=2$, and write the consumer's utility function as $u(x_1,x_2)=x_1+\eta(x_2)$. Now, however, let the consumption set be $R_+^2$, so that there is a nonnegative constraint on consumption of the numeraire $x_1$. Fix prices $p$, and examine how the consumer's Walrasian demand changes as wealth $w$ varies. When is the nonnegativity constraint on the numeraire irrelevant?

My question is about the (c) part: 1. what does it mean? 2. Can anyone explain the solution to it?

The solution to (c) (from the solution manual) is:

The non-negativity constraint is binding if and only if $p_2x_2(p,0)>w$. Note that $x_2(p,0)=(\eta')^{-1}(p_2)$, because $p_1=1$. Hence the constraint is binding if and only if $p_2(\eta')^{-1}(p_2)>w$. If so, the Walrasian demand is given by $x(p,w)=(0,w/p_2)$. Thus, as $w$ changes, the consumption level of the first good is unchanged and the consumption of the second good changes at rate $1/p_2$ with $w$ until the non-negativity constraint no longer binds.

In the original problem, for $L=2$, the consumption set was $(-\infty,\infty) \times \mathbb{R}_{+}$. Now, the consumer is restricted to $[0,\infty)\times \mathbb{R}_{+} = \mathbb{R}_{+}^2$. Fix a price $p = (1,p_2)$, and now see what happens to Walrasian demand (i.e. $x(p,w) = (x_1,x_2))$ as we vary $w$.
You saw from part (a) that a consumer with these preferences consume in the following order.

(i) Spend all money on good 2 until we get to $x_2(p,0)$ (since the amount of good 2 demanded is independent of $w$).

(ii) If you have money leftover, spend it on good 1 (i.e. $x_1(p,w) \geq 0$). Otherwise, in order to balance the budget, consume negative amounts of good 1 (i.e. $x_1(p,w) < 0$).

Let us now fix $p = (1,p_2)$. Now, we are unable to do case (ii) when you have overspent on good 2. You can only overspend when:
$$p_2 \times x_2(p,0) > w$$

Using the usual marginal rate of substitution equal price ratio, we have:
$$
\frac{\eta'(x_2)}{1} = \frac{p_2}{1}
$$
Hence,
$$x_2(p,0) = \eta'^{-1}(p_2)$$

Going back to the first equation, $p_2\times \eta'^{-1}(p_2) > w$.

Since she spends all her money on good 2, the budget constraint
$$x_1(p,w) + p_2 x_2(p,0) = w$$
becomes
$$p_2 x_2(p,0) = w$$
or
$$x_2(p,0) = \frac{w}{p_2}$$

Hence, her Walrasian demand (up to $p_2\times \eta'^{-1}(p_2) > w$) is
$$x(p,w) = (0,\frac{w}{p_2})$$
And once $w$ is large enough such that $p_2\times \eta'^{-1}(p_2) \leq w$,
$$x(p,w) = (w - x_2(p,0),x_2(p,0))$$

EDIT: Lets try a different way to get the answer using the calculus approach.

Our new maximisation problem is:
\begin{align*}
\max_{x_1,x_2} \, &x_1 + \eta(x_2) \\
\text{s.t. } &x_1 + p_2 x_2 = w &\text{ (Budget constraint)}\\
&x_1 \geq 0 &\text{ (Non-negativity constraint) }
\end{align*}
I put equality on the budget constraint because I assume monotonicity.
The associated Lagrangian is
$$
\mathcal{L} = x_1 + \eta(x_2) + \lambda(w - x_1 - p_2 x_2) + \mu(x_1)
$$
Taking first order conditions:
$$
1 - \lambda + \mu = 0 \\
\eta'(x_2) - p_2\lambda = 0
$$
Now, suppose the non-negativity constraint does not bind (i.e. $x_1 > 0$). Then, by complimentary slackness, $\mu = 0$, which implies $\lambda = 1$ and $x^*_2 = \eta'^{-1}(p_2)$.
We can clearly see that the optimal choice of $x_2$ does not depend on $w$ at all when the constraint is non-binding.

Now, suppose the non-negativity constraint does bind (i.e. $x_1 = 0$). Using the budget constraint, $0 + p_2x_2 = w$, thus $x_2 = \frac{w}{p_2}$.

Finally, when does the non-negativity constraint matter? It matters only when we would like to set $x_1 < 0$. Again, using the budget constraint,
$$
0 > x_1 = w - p_2x_2^* = w - p_2 \eta'^{-1}(p_2) \Leftrightarrow w < p_2 \eta'^{-1}(p_2)
$$

Hence, when $w < p_2 \eta'^{-1}(p_2)$, the constraint binds thus $x_1 = 0$ and $x_2 = \frac{w}{p_2}$.

When $w \geq p_2 \eta'^{-1}(p_2)$, the constraint does not bind thus $x_2 = \eta'^{-1}(p_2)$ and $x_1 = w - \eta'^{-1}(p_2) \geq 0$.


Does higher tax on luxury goods does more harm than benefit? I was studying Fiscal policy and I came across measures to reduce income inequality by using fiscal tools. One of the tool mentioned in that was - Higher taxes on luxury goods

This makes sense that luxury good are enjoyed by rich so imposing higher taxes is a sound and prudent measure to take for welfare of poor but here is my thought -
Let's say if we don't impose high tax on luxury goods then it would drive more consumption which in turn will cause firms to hire labor for production and poor will benefit with employment and better income opportunities. Government will also benefit from the taxes - both production and consumption taxes! so Wouldn't it be more feasible? Raising incomes of poor through employment is far better.

Also, I believe the reason why it is not done because government doesn't want to be seen as favouring business or corporates over poor which profoundly mars the chance of political party.

Please share your thoughts, critique and if possible help me find fault in my line of thinking.

Note - In India when government imposed highest tax bracket on luxury cars, cars sales did see a slump so the argument that rich will continue to consume regardless of taxes might not hold true broadly also.

Luxury goods tend to be Veblen goods, so reducing taxes on them doesn't make them more attractive. They also tend to be goods whose value is mainly based on scarcity, so increasing demand doesn't increase the amount supply, it just increases the equilibrium price.  If diamonds were cheaper, would there be significantly more diamond jobs?

There's the further issue that taxes just move money around. Giving rich people a tax break means either raising taxes on poorer people, reducing government spending, and/or deficit spending (which reduces the amount of capital available for private investment).