Author: Zachary Bartsch

Children Are Not 3/55ths of a Person

Zachary Bartsch1 Comment

In the past several years there has been increasing salience and support of pronatalist policies. Several people have turned to the IRS income tax code, which already includes some incentives regarding children. The Child Tax Credit (CTC), which lowers a person’s tax liability on a dollar-per-dollar basis, is the most obvious item that addresses children. The other tax credit is for child care expenses, but I won’t be focusing on that here.

Below are the 2021 marginal tax rate brackets and the standard deductions.  The standard deduction reduces the taxable income, and then the tax rates are applied.

After the tax liability is calculated, it’s reduced by any tax credits, such as the CTC. In 2021, households earned a credit of $3,600 for every child under 6 years old and $3,000 for every child under 18 years old.  Median household income in 2020 was $67,521.  That means that the tax liability was reduced by 5.3% – or 3/55ths – of median gross income. But, I have a problem with that.

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Covid-19 Didn’t Break the Supply Chains. You Did.

Zachary Bartsch4 Comments

This is my last post in a series that uses the AS-AD model to describe US consumption during and after the Covid-19 recession. I wrote about US consumption’s broad categories, services, and non-durables. This last one addresses durable consumption.

During the week of thanksgiving in 2020, our thirteen-year-old microwave bit the dust. NBD, I thought. Microwaves are cheap, and I’m willing to spend a little more in order to get one that I think will be of better quality (GE, *cough*-*cough*). So, I filtered through the models on multiple websites and found the right size, brand, and wattage. No matter the retailer, at checkout I learned that regardless of price, I’d be waiting a good two months before my new, entirely standard, and unexceptional microwave oven would arrive. I’d have to wait until the end of January of 2021.

¡Que Ridiculo!

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AS-AD: From Levels to Percent

Zachary Bartsch9 Comments

The aggregate supply & aggregate demand model (AS-AD) is nice because it’s flexible and clear. Often professors will teach it in levels. That is, they teach it with the level of output on one axis, and the price level on the other axis. This presentation is convenient for the equation of exchange, which can be arranged to reflect that aggregate demand (AD) is a hyperbola in (Y, P) space. Graphed below is the AD curve in 2019Q4 and in 2020Q2 using real GDP, NGDP, and the GDP price deflator.

The textbook that I use for Principles of Macroeconomics, instead places inflation (π) on the vertical axis while keeping the level of output on the horizontal axis. The authors motivate the downward slope by asserting that there is a policy reaction function for the Federal Reserve. When people observe high rates of inflation, state the authors, they know that the Fed will increase interest rates and reduce output. Personally, I find this reasoning to be inadequate because it makes a fundamental feature of the AS-AD model – downward sloping demand – contingent on policy context.

At the same time, I do think that it can be useful to put inflation on the vertical axis. Afterall, individuals are forward looking. We expect positive inflation because that’s what has happened previously, and we tend to be correct. So, I tell my students that “for our purposes”, placing inflation on the vertical axis is fine. I tell them that, when they take intermediate macro, they’ll want to express both axes as rates of change. I usually say this, and then go about my business of teaching principles.

But, what does it look like when we do graph in percent-change space?

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In Praise of the FRED Excel Add-in

Zachary Bartsch2 Comments

Sometimes, large entities have enough money to throw at a problem that by sheer magnitude they produce something great (albeit at too high a cost). The iPhone app from the FRED is not that thing. But the Excel add-in is something that every macroeconomics professor should consider adding to their toolkit.

Personally, I include links to FRED content in the lecture notes that I provide to students. But FRED makes it easy to do so much more. They now have an add-in that makes accessing data *much* faster. With it, professors can demonstrate in excel their transformations that students can easily replicate. The advantage is that students can learn to access and transform their own data rather than relying on links that I provide them.

The tool is easy enough to find – FRED wants you to use it. After that, the installation is largely automatic.

Installed in excel you will see the below new ribbon option. It’s very user friendly.

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Dressed for Recess(ion)

Zachary Bartsch1 Comment

In my previous post, I decomposed consumer expenditures to figure out which service sectors experienced the largest supply-side disruptions due to Covid-19. I illustrated that transportation & recreation services were the only consumer service to experience substantial and persistent supply shocks. Health, food, and accommodation services also experienced supply shocks, but quickly rebounded. Housing, utility, and financial services experienced no supply disruptions whatsoever.

What about non-durables?

Total consumption spending is the largest category of spending in our economy and is composed of services, durable goods, and non-durables. Services are the largest portion and durable goods compose the smallest portion. So, while there were plenty of stories during the Covid-19 pandemic about months-long delivery times for durables, they did not constitute the typical experience for most consumption.

Even though it’s not the largest category, many people think of non-durables when they think of consumption. Below is the break-down of non-durable spending in 2019. The largest singular category of non-durable spending was for food and beverages, followed by pharmaceuticals & medical products, clothing & shoes, and gasoline and other energy goods. Clearly, the larger the proportion that each of these items composes of an individual household budget, the more significant the welfare implications of price changes.

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It’s Still Hard to Find Good Help These Days

Zachary Bartsch2 Comments

Consumption is the largest component of GDP. In 2019, it composed 67.5% of all spending in the US. During the Covid-19 recession, real consumption fell about 18% and took just over a year to recover. But consumption of services, composing 69% of consumption spending, hadn’t recovered almost two years after the 2020 pre-recession peak.  For those keeping up with the math, service consumption composed 46.5% of the economic spending in 2019.

We can decompose service consumption even further. The table below illustrates the breakdown of service consumption expenditures in 2019.

I argued in my previous post that the Covid-19 pandemic was primarily a demand shock insofar as consumption was concerned, though potential output for services may have fallen somewhat. When something is 67.5% of the economy, ‘somewhat’ can be a big deal. So, below I breakdown services into its components to identify which experienced supply or demand shocks. Macroeconomists often get accused of over-reliance on aggregates and I’ll be a monkey’s uncle if I succumb to the trope (I might, in fact be a monkey’s uncle).

Before I start again with the graphs, what should we expect? Let’s consider that the recession was a pandemic recession. We should expect that services which could be provided remotely to experience an initial negative demand shock and to have recovered quickly. We should expect close-proximity services to experience a negative demand and supply shock due to the symmetrical risk of contagion. Finally, we should expect that services with elastic demand to experience the largest demand shocks (If you want additional details for what the above service categories describe, then you can find out more here, pg. 18).

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Amazon Credit Card Rewards

Zachary Bartsch4 Comments

I have a credit card that gives me rewards. I get a nice 5% cash-back on purchases from Amazon and a lower cash-back rate on other purchases. Sometimes, there are promotions that provide a rate of 10% or even 15%. But what are these rewards worth?

To simplify, there are two reward options:

Option 1 adds to my Amazon gift-card balance. It’s attractive. When I’m checking out at Amazon, it shows me my reward balance and it also shows me what the total cost of my purchase could be if I applied the gift card. It’s like they’re trying to pressure me to redeem my rewards in this particular way.

Option 2 is simply to transfer my rewards as a payment on my credit card or as a credit to my bank account (for the current purposes, they’re identical). Either way, the rewards translate to the same number of dollars.

Say that I spend $1,000 at Amazon. Whether I choose option 1 or 2 has value implications.

Option 1

The calculation is simple. If I spend $1,000 at amazon this month, then I can spend another $50 in gift card credits at Amazon next month. That’s the end. There are no more relevant cashflows. I used my credit card one month, and then was rewarded the next month. The only detail worth adding is the time value of money, which at 7% per year*, yields a present value of rewards at $49.72. Option 1 is nice in the moment. It’s so enticing to have a lower Amazon check-out balance due.

But you should never select Option 1.

Option 2

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Covid Evidence: Supply Vs Demand Shock

Zachary Bartsch4 Comments

By the time most students exit undergrad, they get acquainted with the Aggregate Supply – Aggregate Demand model. I think that this model is so important that my Principles of Macro class spends twice the amount of time on it as on any other topic. The model is nice because it uses the familiar tools of Supply & Demand and throws a macro twist on them. Below is a graph of the short-run AS-AD model.

Quick primer: The AD curve increases to the right and decreases to the left. The Federal Reserve and Federal government can both affect AD by increasing or decreasing total spending in the economy. Economists differ on the circumstances in which one authority is more relevant than another.

The AS curve reflects inflation expectations, short-run productivity (intercept), and nominal rigidity (slope). If inflation expectations rise, then the AS curve shifts up vertically. If there is transitory decline in productivity, then it shifts up vertically and left horizontally.

Nominal rigidity refers to the total spending elasticity of the quantity produced. In laymen’s terms, nominal rigidity describes how production changes when there is a short-run increase in total spending. The figure above displays 3 possible SR-AS’s. AS0 reflects that firms will simply produce more when there is greater spending and they will not raise their prices. AS2 reflects that producers mostly raise prices and increase output only somewhat. AS1 is an intermediate case. One of the things that determines nominal rigidity is how accurate the inflation expectations are. The more accurate the inflation expectations, the more vertical the SR-AS curve appears.*

The AS-AD model has many of the typical S&D features. The initial equilibrium is the intersection between the original AS and AD curves. There is a price and quantity implication when one of the curves move. An increase in AD results in some combination of higher prices and greater output – depending on nominal rigidities. An increase in the SR-AS curve results in some combination of lower prices and higher output – depending on the slope of aggregate demand.

Of course, the real world is complicated – sometimes multiple shocks occur and multiple curves move simultaneously. If that is the case, then we can simply say which curve ‘moved more’. We should also expect that the long-run productive capacity of the economy increased over the past two years, say due to technological improvements, such that the new equilibrium output is several percentage points to the right. We can’t observe the AD and AS curves directly, but we can observe their results.

The big questions are:

  1. What happened during and after the 2020 recession?
  2. Was there more than one shock?
  3. When did any shocks occur?

Below is a graph of real consumption and consumption prices as a percent of the business cycle peak in February prior to the recession (See this post that I did last week exploring the real side only). What can we tell from this figure?

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This Time was Way Different

Zachary Bartsch5 Comments

The financial crisis recession that started in late 2007 was very different from the 2020 pandemic recession. Even now, 15 years later, we don’t all agree on the causes of the 2007 recession. Maybe it was due to the housing crisis, maybe due to the policy of allowing NGDP to fall, or maybe due to financial contagion. I watched Vernon Smith give a lecture in 2012 in which he explained that it was a housing crisis. Scott Sumner believes that a housing sectoral decline would have occurred, and that the economy-wide deep recession and subsequent slow recovery was caused by poor monetary policy.

Everyone agrees, however, that the 2007 recession was fundamentally different from the 2020 recession. The latter, many believe, reflected a supply shock or a technology shock. Performing social activities, including work, in close proximity to others became much less safe. As a result, we traded off productivity for safety.

The policy responses to each of the two were also different. In 2020, monetary policy was far more targeted in its interventions and the fiscal stimulus was much bigger. I’ll save the policy response differences for another post. In this post, I want to display a few graphs that broadly reflect the speed and magnitude of the recoveries. Because the recessions had different causes, I use broad measures that are applicable to both.

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Russia, The US, and Crude Data

Zachary Bartsch4 Comments

Overall, I’ve been disappointed with the reporting on the US embargo against Russian oil. The AP reported that the US imports 8% of Russia’s crude oil exports. But then they and other outlets list a litany of other figures without any context for relative magnitudes. Let’s shine some more light on the crude oil data.*

First, the 8% figure is correct – or, at least it was correct as of December of 2021. The below figure charts the last 7 years of total Russian crude oil exports, US imports of Russian crude oil, and the proportion that US imports compose.  That 8% figure is by no means representative of recent history. The average US proportion in 2015-2018 was 7.8%. But the US share as since risen in level and volatility. Since 2019, the US imports compose an average of 11.9% of all Russian crude oil exports.

As an exogenous shock, the import ban on Russian crude oil might have a substantial impact on Russian exports. However, many of the world’s oil importers were already refusing Russian crude. The US ban may not have a large independent effect on Russian sales and may be a case of congress endorsing a policy that’s already in place voluntarily.

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