Data

When Will the Fed Raise Rates?

James Bailey4 Comments

Everyone else keeps asking when the Fed will cut rates, and yesterday Chair Powell said they will likely cut this year. Either they are all crazy or I am, because almost every indicator I see indicates we are still above the Fed’s inflation target of 2% and are likely to remain there without some change in policy. Ideally that change would be a tightening of fiscal policy, but since there’s no way Congress substantially cuts the deficit this year, responsibility falls to the Federal Reserve.

Source: https://fiscaldata.treasury.gov/americas-finance-guide/national-deficit/

Lets start with the direct measures of inflation: CPI is up 3.1% from a year ago. The Fed’s preferred measure, PCE, is up 2.4% from a year ago. Core PCE, which is more predictive of where inflation will be going forward, is up 2.8% over the past year. The TIPS spread indicates 2.4% annualized inflation over the next 5 years. The Fed’s own projections say that PCE and Core PCE won’t be back to 2.0% until 2026.

The labor market remains quite tight: the unemployment rate is 3.7%, payroll growth is strong (353,000 in January), and there are still substantially more job openings than there are unemployed workers. The chattering classes underrate this because they are in some of the few sectors, like software and journalism, where layoffs are actually rising. Real GDP growth is strong (3.2% last quarter), and nominal GDP growth is still well above its long-run trend, which is inflationary.

I do see a few contrary indicators: M2 is still down from a year ago (though only 1.4%, and it is up over the past 6 months). The Fed’s balance sheet continues to shrink, though it is still trillions above the pre-Covid level. Productivity rose 3.2% last quarter.

But overall I am still more worried about inflation than about a recession, as I was 6 months ago. Financial conditions have changed dramatically from a year ago, when the discussion was about bank runs and a near-certain recession. Today the financial headlines are about all time highs for Bitcoin, Gold, Japan, and US stocks, with an AI-fueled boom (bubble?) in tech pushing the valuation of a single company, Nvidia, above the combined valuation of the entire Chinese stock market. All of this screams inflation, though it could also indicate a recession in a year or so if the bubble pops.

At least over the past year I think fiscal policy is more responsible than monetary policy for persistent inflation. But I can’t see Congress doing a deficit-reducing grand bargain in an election year; the CBO projects the deficit will continue to run over 5% of GDP. That means our best chance for inflation to hit the target this year is for the Fed to tighten, or at least to not cut rates. If policy continues on its current inflationary path, our main hope is for a deus-ex-machina like a true tech-fueled productivity boom, or deflationary events abroad (recession in China?) lowering prices here.

Shrinkflation: Not Just for Cookies

Jeremy Horpedahl2 Comments

Cookie monster is mad:

But he’s not the only one. President Biden is mad too.

By now, hopefully we’ve all heard of shrinkflation. But if you haven’t, it’s when the unit price (e.g., the cost per pound) increases not because the price of the good went up, but because the product shrank in size.

Let’s be clear about a few things. First, this is nothing new. Here’s an Economist story from 2019 (pre-pandemic and pre-Bidenflation) talking about shrinkflation. You can find many such anecdotal stories back even further.

Second, the BLS is aware of this. They track it, and price it into the CPI. Take a look at the price data which underlies the CPI: it’s all stated in units. Price per pound, price per dozen, etc.

Moreover, the BLS also recently gave us some data on how frequently this happens. It’s pretty rare. Even among food items, which are a category the includes a fair amount of shrinkflation, only about 3 percent of products experienced any downsizing or upsizing from 2015-2021. That’s right, sometimes packages get larger, not smaller, which effectively lowers the unit price. “Shrinkdeflation” anyone?

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Alabama’s Homicide Rate is More than Double New York City

Jeremy Horpedahl2 Comments

A lot of people think New York City is an especially high-crime city. Including some US Senators. Here’s senior Senator from Alabama:

Ignore the weird obsession with Biden’s ice cream habit. The Senator is concerned that NYC is not safe.

But what’s the reality? Here’s a map showing the homicide rate in each state, and its relative position to NYC (data is from the CDC for 2022, the most recent complete year available right now).

The light-colored states have a lower homicide rate than NYC (5.2 deaths per 100,000). There’s 18 of those states. But most states have higher homicide rates than NYC. Some are a lot higher, even triple NYC in a few states (colored purple). Alabama’s homicide rate of 13.9 deaths per 100,000 people is about 2.5 times as high as New York City.

But perhaps the homicide rates in these states are being driven by high homicide rates in cities in those states? Comparing a city to a state is perhaps a little strange to do, but I also often hear this retort: well, it’s those cities, especially “Democrat-controlled” cities, that are driving the high homicide rate in Alabama and elsewhere. And while this is true to a certain extent, comparing rural counties to New York City doesn’t make Alabama and the South look much better:

For this map I combined 2021 and 2022 data, because the CDC doesn’t report very small numbers (usually under 10 deaths), so grouping two years is needed to get more data. Even so, there are still a handful of states that don’t have enough homicides for CDC to report them over that two-year period, and they are shown in gray on the map (as well as states that have no rural counties: Delaware, Rhode Island, and New Jersey).

Notice that even focusing on just the rural counties, there are almost 20 states with higher murder rates than New York City. Again, some are double or even triple. Rural Alabama, at 11 deaths per 100,000 people, is exactly double NYC. Notably, the entirety of the rural South is higher than NYC.

If this is all true, why might New York City feel less safe? There are a number of possible explanations, but I’ll offer a few. First, homicide isn’t the only kind of crime. While it does correlate with other crimes, it’s not a 1:1 relationship, so it’s likely that some places with higher homicide rates than NYC have lower levels of assault, rape, or property crimes. These are even more challenging to compare across jurisdictions, but it’s a possible explanation. Related, NYC is a relatively safe big city! Other big cities wouldn’t compare as favorably to Alabama. But folks just seem to love NYC as a punching bag.

The other explanation is just the sheer number of people, and therefore homicides. According to the CDC, NYC had 434 homicides in 2022, that’s an average of more than one per day. You could literally turn on the news every single day and hear about a murder, and perhaps you had even been in the neighborhood where it happened recently. Contrast rural Alabama, which had 65 homicides in 2022. That’s only about one per week. And it might be happening in a completely different part of the state from you, so you either don’t hear about it or think “that’s somewhere else.”

But rural Alabama only has about 600,000 people. NYC has fourteen times as many people. So if we are trying to answer the question “What are the odds that a random person is murdered in a given year?”, we need to take population into account. That’s the logic of reporting homicide rates. Indeed it may feel like NYC is less safe, and that’s a natural human reaction. But that’s why the data is so important, to give us a sense of proportion.

Food Inflation in the G7 and Russia

Jeremy Horpedahl1 Comment

Food prices are up a lot in the past few years. I’ve written about this several times in the past few months. In the US, we’ve seen grocery prices go up 20% on average in just 3 years. That’s much higher than we are used to: in the decade before the pandemic, the average 3-year increase was just 4%. In fact, the 3-year increase was negative for much of 2017 and 2018. To find increases this big, you have to go back to the late 1970s and early 1980s (when sometimes the 3-year grocery inflation rate was almost 50%).

But if it’s any consolation, this is not a problem that is unique to the US: food prices are up around the globe. That’s a relevant insight when we come to a recent viral video from Tucker Carlson’s visit to a Russian grocery store. Carlson says that the inflation and cost of groceries will “radicalize you against our leaders.”

So what has food price inflation looked like in Russia, the US, and the other G7 countries? (What used to be called the G8, until Russia invaded Crimea in 2014.) Here’s the chart:

Cumulatively since January 2021, when our current “leaders” came into power in the US, food prices are up 20% in the US, as I said above. But notice that this is on the low end for this group of countries. Japan, with consistently low inflation and occasionally deflation over the past few decades, has been the lowest over this timeframe (though even in Japan, food prices are up about 7 percent in the past year).

But notice who is the highest: Russia, where grocery prices are up 32% in the past 3 years. Certainly, their invasion of Ukraine and the resulting global sanctions plays a role in this, but even if we look at early 2022, the cumulative 15% food inflation was much higher than any G7 country.

So blaming our leaders for rampant inflation is probably not a good idea, especially if you are trying to portray Russia in a positive light.

Perhaps the more charitable interpretation of Tucker Carlson is that the nominal price of groceries is lower, rather than the rate of inflation (even though he does mention inflation in the video). The basket of food they purchase in the video comes out to the equivalent of about $100 at current exchange rates. Everyone on his crew guessed it would be around $400.

I can’t say whether their guess of $400 was accurate, but it would not be totally surprising if the prices of non-tradable goods were lower. This is what would expect in a country with lower wages. While we normally think of services as non-tradable, it’s also reasonable to assume that a lot of fresh food, such as produce, bread, and dairy, is also non-tradable (at least not without high transaction costs).

Carlson’s claim that people “literally can’t buy the groceries they want” is a much more apt statement of the state of affairs in Russia (and other poor countries) than it is in the US and Western Europe.

We can see this in a few ways. For example, here’s a chart showing the percent of consumer spending that goes to groceries:

The average Russian allocates about 30% of their spending to groceries, similar to the Dominican Republic. And this data is from 2021, just before the massive spike in food prices in Russia. Meanwhile, the US is by far the lowest, at just under 7%. The UK, Canada, and Switzerland are the closest to the US, but they are in the 9-10% range. Food in the US is cheap.

And those high average levels from Russia obscure a wide-ranging distribution of food insecurity. In a story from Russian state-owned news agency TASS, they report that over 60% of Russians spend at least half of their monthly income on food. Even Putin is publicly acknowledging that inflation is a problem.

The food inflation we’ve experienced in the US has been bad, the worst in a generation. But it’s not exactly clear that our “leaders” are to blame. And it’s also pretty clear that it’s much worse in the rest of the world, especially in Russia.

Covid Death Structural Breaks

Zachary BartschLeave a comment

xtbreak (STATA)

I found a new time series and panel data tool that I want to share. What does it do? It’s called xtbreak and it finds what are known as ‘structural breaks’ in the data. What does that mean? It means that the determinants of a dependent variable matter differently at different periods of time. In statistics we’d say that the regression coefficients are different during different periods of time. To elaborate, I’ll walk through the same example that the authors of the command use.

You can download the time series data from here: https://github.com/JanDitzen/xtbreak/blob/main/data/US.dta

The data contains weekly US covid cases and deaths for 2020-2021. Here’s what it looks like:

So, what’s the data generating process? It stands to reason that the number of deaths is related to the number of cases one week prior. So, we can adopt the following model:

That seems reasonable. However, we suspect that δ is not the same across the entire sample period. Why not? Medical professionals learned how to better treat covid, and the public changed their behavior so that different types of people contracted covid. Further, once they contracted it, the public’s criteria for visiting the doctor changed. So, while the lagged number of cases is a reasonable determinant of deaths across the entire sample, we would expect it to predict a different number deaths at different times. In the model above, we are saying that δ changes over time and maybe at discrete points.

First, xtbreak allows us to test whether there are any structural breaks. Specifically, it can test whether there are S breaks rather than S-1 breaks. If the test statistic is greater than the critical statistics, then we can conclude that there are some number of breaks. Note that there being 5 breaks given that there are 4 depends on there also be at least 4 breaks. And since we can’t say that there are certainly 4 breaks rather than 3, it would be inappropriate to say that there are 4 or 5 breaks.

Great, so if there are three structural breaks, then when do they occur? xbtreak can answer that too (below). The three structural breaks are noted as the 20th  week of 2020, the 51st week of 2020, and the 11th week of 2021. Conveniently, there is also a confidence interval. Note that the confidence intervals for 2020w11 and 2021w11 breaks are nice and precise with a 1-week confidence interval. The 2nd break, however, has a big 30-week confidence interval (nearly 7 months). So, while we suspect that there is a 3rd  structural break, we don’t know as precisely where it is.

Regardless, if there are three structural breaks, then that means that there are four time periods with different relationships between lagged covid cases and covid deaths. We can create a scatter plot of the raw data and run a regression to see the different slopes. Below we can see the different slopes that describe the impact of lagged covid cases on deaths. Sensibly, covid cases resulted in more deaths earlier during the pandemic. As time passed, the proportion of cases which resulted in death declined (as seen in the falling slope of the dots). It’s no wonder that people were freaking out at the start of the pandemic.

What’s nice about this method for finding breaks is that it is statistically determined. Of course, it’s important to have a theoretical motivation for why any breaks would occur in the first place. This method is more rigorous than eye-balling the data and provides opportunities to hypothesis test the number of breaks and their location. If you read the documentation, then there are other tests, such as breaks in the constant, that are also possible.

See this ppt by the authors for more: https://www.stata.com/meeting/germany21/slides/Germany21_Ditzen.pdf

See this Stata Journal article for more still: https://repec.cal.bham.ac.uk/pdf/21-14.pdf

Go East, Young Man

James Bailey1 Comment

Americans have moved westward in every decade of our history. But after over 200 years, that trend may finally be ending.

A new report from Bank of America notes that the share of Americans who live in the West has been falling since 2020:

The absolute population of the West is still growing slightly, but the Southeast is growing so quickly that it makes every other region of the country a smaller share by comparison:

I think this has a lot to do with the decline in housing affordability that Jeremy discussed yesterday. Americans always went West for free land, or cheap land, or cheap housing. Or in more recent decades on the Pacific coast, they went for nice weather and good jobs with non-insane housing prices. But now all that is gone, and if anything housing prices are pushing people East.

I see some green shoots of zoning reform with the potential to lower housing costs in the West. But I worry that this is too little too late, and that 2030 will confirm that our long national trek Westward has finally been defeated by our own poor housing policy.

The US Housing Market Is Very Quickly Becoming Unaffordable

Jeremy Horpedahl15 Comments

In a post from July 2021, I discussed housing affordability and “zoning taxes” — in other words, how land use restrictions such as zoning were driving up the cost of housing in some US cities. San Francisco, Los Angeles, Seattle, and New York stood out as the clear outliers, with “zoning taxes” adding several multiples of median household income to housing costs.

The paper I was summarizing used data from 2013-2018, and it’s a very well done paper. But so much has changed in the US housing market since that time. In my post, I pointed to a map from 2017 showing that a large swatch of the interior country still had affordable housing — loosely defined as median home prices being no more than 3 times median income.

To see how much has changed so quickly, consider these two maps for 2017 and 2022 generated from this interactive tool from the Joint Center for Housing Studies.

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Medicaid Cuts Mean Credit Card Debt

James BaileyLeave a comment

My paper “Missouri’s Medicaid Contraction and Consumer Financial Outcomes” is now out at the American Journal of Health Economics. It is coauthored by Nate Blascak and Slava Mikhed, researchers at the Federal Reserve Bank of Philadelphia. They noticed that Missouri had done a cut in 2005 that removed about 100,000 people from Medicaid and reduced covered services for the remaining enrollees. Economists have mostly studied Medicaid expansions, which have been more common than cuts; those studying Medicaid cuts have focused on Tennessee’s 2005 dis-enrollments, so we were interested to see if things went differently in Missouri.

In short, we find that after Medicaid is cut, people do more out-of-pocket spending on health care, leading to increases in both credit card borrowing and debt in third-party collections. Our back-of-the-envelope calculations suggest that debt in collections increased by $494 per Medicaid-eligible Missourian, which is actually smaller than has been estimated for the Tennessee cut, and smaller than most estimates of the debt reduction following Medicaid expansions.

We bring some great data to bear on this; I used the restricted version of the Medical Expenditure Panel Survey to estimate what happened to health spending in Missouri compared to neighboring states, and my coauthors used Equifax data on credit outcomes that lets them compare even finer geographies:

The paper is a clear case of modern econometrics at work, in that it is almost painfully thorough. Counting the appendix, the version currently up at AJHE shows 130 pages with 29 tables and 11 figures (many of which are actually made up of 6 sub-figures each). We put a lot of thought into questioning the assumptions behind our difference-in-difference estimation, and into figuring out how best to bootstrap our standard errors given the small number of clusters. Sometimes this feels like overkill but hopefully it means the final results are really solid.

For those who want to read more and can’t access the journal version, an earlier ungated version is here.

Disclaimer: The results and conclusions in this paper are those of the authors and do not indicate concurrence by the Agency for Healthcare Research and Quality or the US Department of Health and Human Services. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Younger Generations Have Higher Incomes Too (and it’s probably not explained by the rise of dual-income families)

Jeremy Horpedahl3 Comments

Regular readers know that I’ve written numerous times about the wealth levels of younger generations, such as this post from last month. Judged by average (and usually median too) wealth, younger generations are doing as well and often better than past generations. This is not too surprising, if you generally think that subsequent generations are better off than their parents, but many people today seem to think that progress has stopped. The data suggest it hasn’t stopped!

Now there’s a great new paper by Kevin Corinth and Jeff Larrimore which looks at not wealth but income levels by generation. The look at income in a variety of different ways, including both market income and post-tax/transfer income. But the result is pretty consistent: each generation has higher incomes (inflation adjusted) than the previous generation. Here’s a typical chart from the paper:

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A Measure of Dissimilarity

Zachary Bartsch1 Comment

I recently learned about an interesting statistic for social scientists. It’s called the “Dissimilarity Index”. It allows you to compare the categorical distribution of two sets.

Many of us already know how to compare two distributions that have only 2 possible values. It’s easy because if you know the proportion of a group who are in category 1, then you know that 1-p will be in category 2. We can conveniently denote these with values of zero and one, and then conduct standard t-tests or z-tests to discover whether they are statistically different. But what about distributions across more than two possible categories?

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