Data

Behavioral Risk Factor Surveillance System Survey: Now in Stata and CSV formats

James Bailey1 Comment

The BRFSS Annual Survey is now available in Stata DTA and Excel-friendly CSV formats at my Open Science Foundation page.

The US government is great at collecting data, but not so good at sharing it in easy-to-use ways. When people try to access these datasets they either get discouraged and give up, or spend hours getting the data into a usable form. One of the crazy things about this is all the duplicated effort- hundreds of people might end up spending hours cleaning the data in mostly the same way. Ideally the government would just post a better version of the data on their official page. But barring that, researchers and other “data heroes” can provide a huge public service by publicly posting datasets that they have already cleaned up- and some have done so.

That’s what I said in December when I added a data page to my website that highlights some of these “most improved datasets”. Now I’m adding the Behavioral Risk Factor Surveillance Survey. The BRFSS has been collected by the Centers for Disease Control since the 1980s. It now surveys 400,000 Americans each year on health-related topics including alcohol and drug use, health status, chronic disease, health care use, height and weight, diet, and exercise, along with demographics and geography. It’s a great survey that is underused because the CDC only offers it in XPT and ASC formats. So I offer it in Stata DTA and Excel CSV formats here.

Let me know what dataset you’d like to see improved next.

The Minimum Wage and Crime

Jeremy Horpedahl2 Comments

The minimum wage is one of the most studied topics in economics, and also something that is frequently discussed on this blog from many different angles. For someone that isn’t an expert in this area, it can be hard to keep track of all the most recent, cutting-edge research on the topic.

Here’s a brand-new paper in the literature with an important finding: raising the minimum wage increases crime. Specifically, in “The Unintended Effects of Minimum Wage Increases on Crime” the authors find that 16-to-24-year-olds commit more property crimes after a minimum wage increase. For every 1% increase in the minimum wage, there is a 0.2% increase in property crime. That implies a doubling the minimum wage would increase property crimes for this age group by 20%. Here’s a figure from the paper showing this increase in crime:

What is the mechanism by which the rising minimum wage increases crime? Here the authors move into examining one of the central questions of the empirical minimum wage debate: the labor market. The authors do find evidence that employment decreases for this same age group following an increase in the minimum wage. Again, a figure from the paper:

The results in this paper add one more element to the cost-benefit calculus of the minimum wage. But I think the results are also interesting because they seem to point in the opposite direction of a paper co-authored by fellow EWED blogger Mike Makowsky. His paper “The Minimum Wage, EITC, and Criminal Recidivism” found that increasing the minimum wage made it less likely that former prisoners would commit another crime. I would be interested to hear Mike’s thoughts on this paper!

The Murky Macro Picture

James Bailey2 Comments

Last June I wondered if we were seeing the peak of inflation, and by at least one major measure I called the peak exactly:

At the moment, though, I’m feeling more confused than prophetic. The big question a year ago was how long it would take the Fed to get inflation down to reasonable levels, and how much collateral damage they would do to the real economy in that effort. Today most current indicators make it look like they pulled off the miraculous “soft landing”. Inflation over the last 12 months is still high, but over the last 6 months we’re nailing the Fed’s 2% annualized target. This has hit a few sectors of the real economy hard, with housing slowing dramatically and tech doing mass layoffs, but the overall picture is great: GDP growth was around 3% the last 2 quarters, and the 3.4% unemployment is the lowest since 1969.

What’s confusing about this is that we have a hard time believing we really got this lucky. Its like your plane lost power, you diverted course for an emergency crash landing, and once you touch down and find yourself seemingly unharmed you look around and wonder if the plane is about to explode. Consumer sentiment is worse than it was in the depths of Covid; business sentiment has been falling for over a year and is almost down to March 2020 levels. Betting markets forecast a 50% chance of a recession in 2023, and the yield curve is strongly inverted (one of the best predictors of a recession, though the guy who first noticed this says it might not work this time):

Finally, M2 money supply is shrinking for the first time since at least 1960, and I believe the first time since the Great Depression. This bodes well for inflation continuing to moderate, but its also one more indicator of a potential recession.

To sum up, most of the indicators of the current state of the economy look great, while most indicators of its near-term future look awful. So which do we trust?

My guess is that we avoid recession in 2023, but honestly this is mostly the gut feeling of an optimist. There’s no one knock-down piece of data I’d point to in support; its more that things are currently going well, and usually the best prediction is that tomorrow will be like today unless you have a good reason to think otherwise. The main reason people expect a slowdown is because of the Fed’s actions to fight inflation. The Fed itself predicts that they will cause a slowdown, but not a recession. Their most recent summary of economic projections from December predicts GDP growth slowing to 0.5% in 2023 and unemployment rising to 4.6%.

I think the “so what” outlook is also murky. Stocks have already fallen a lot from their highs and a recession already seems somewhat ‘priced in’, so even if I thought one was coming I wouldn’t necessarily sell stocks. On the flip side US stocks are still quite expensive by historical standards, so I don’t want to buy more on the assumption that they’ll rise more on good economic news this year. You might want to lock in decent rates on long-term bonds if you think the Fed will cut rates in response to a recession, but the inverted yield curve shows this is already somewhat priced in. 1-year bonds yielding almost 5% seems decent in either scenario, I have some and I’ll probably buy more, but 5% returns are nothing to get excited about. I’d like to hear suggestions but to me the small direct betting market on a potential recession is the clearest “so what” for anyone who does have a confident view about this year’s macro picture.

The Growth of Black Families Income

Jeremy Horpedahl4 Comments

Black families are the poorest major racial or ethnic group in the US. With a median income of $59,541, a Black family only has about 59 percent of the income of a White, Non-Hispanic family. That’s the same proportion, 59 percent, as was true in 1972, the earliest date that we have comparable data. (For most of the data in this post, I will be using Table F-23 from the Census Bureau’s Historical Income Tables.) That’s almost 50 years with no closing of the racial gap in total money income for families.

Of course, what this also means is that family incomes of both Black and Whites grew at the same rate from 1972 to 2021. They both are about 50 percent larger than in 1972, and that’s after accounting for inflation (using the CPI-U-RS). As a first point of optimism, this very much goes against the typical narrative of income stagnation since the early 1970s.

To be sure, some of this growth is because families have more earners today, but even so: they have a lot more income. Having two earners does mean that you must spend more on some consumption categories, such as daycare when kids are young, possibly more on dining out or prepared meals. But even with those additional expenses, these families will have significantly more disposable income than their 1970s counterparts.

There is an ever more optimistic fact that we need to point out for Black families today: there are many, many more rich Black families today than in 1972. There are more rich families in absolute terms and as a proportion of the total. Here is the basic data from the Census Bureau (it goes back to 1967, the earliest date available for Blacks).

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The Social Drug of Prohibition

Zachary Bartsch1 Comment

Why does the average drinker consume alcohol? There are plenty of reasons, one of which is social. Alcohol, while inhibiting clarity, precision, and discretion, is a social lubricant. If you’re one of those drinking, then it’s enjoyable to be around other drinkers. Also, people build the habit of drinking *something* while socializing. We all know that prohibition resulted in bootlegging and tainted cocktails. But what were the legal alternatives? One was that you could purchase grape juice and make your own wine (that’s a story for another time). Another is to switch to another drug.

Alcohol is a depressant and arguably the most popular one in the US. It’s not a clear substitute for alcohol in terms of its direct effects on the body. However, it’s a liquid, safe, and tasty. That make is a good candidate for satisfying the physical urge to imbibe. But, importantly, it is also a social drug. People would get so hopped up on coffee and feed off of one another’s high that Charles the II of England banned coffee houses in order to prevent seditious fomentation. This brings us to an important characteristic of coffee. It’s a stimulant. You’d think that a stimulant would not be a substitute for alcohol. If anything, one might think that they are complements. Coffee helps to provide that kick in the pants after having an enjoyable night. But, the social feature makes coffee a good candidate to substitute alcohol, should the times be dire.

Illegal activity aside, people wanted an outlet for their physical and social proclivities. They wanted intoxication. Coffee provided exactly that. Conveniently, the continental US didn’t grow any of its own coffee. That means that imports and domestic consumption have a tight relationship.

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Economic Recovery from the Pandemic

Jeremy Horpedahl3 Comments

How well have countries recovered from the declines in the pandemic? It’s actually a bit difficult to answer that question, because it depends on how you measure it. Even if we agree that GDP is the best measure, how do we measure recovery? One possibility is to simply ask whether the country has exceeded its pre-pandemic GDP level. Exactly which quarter to use as the baseline is debatable, but here is a chart that Joseph Politano made for G7 countries using the 3rd quarter of 2019 as the baseline.

But we know that absent the pandemic, most countries would have continued growing (absent a recession for some other reason), so just getting back to pre-pandemic levels isn’t necessarily a full recovery. But how much growth should we have expected? It’s a hard question, but here’s a chart along those lines from the Washington Post, using the CBO’s measure of “potential GDP” as what growth might have looked like.

Using either of these approaches, it appears that the US has recovered pretty well, although it would be nice to have a comparison across countries using the same approach as the Washington Post chart does. While there is no consistent measure similar to CBO’s potential GDP figure for all countries, a simple approach is to project growth forward using the average pre-pandemic growth rate. I have done so for a number of countries, using the average growth rate from 2017-2019. In the following charts, the blue line is actual GDP levels, and the orange line is projecting the 2017-2019 growth rate forward. Sorry that I can’t easily fit all these into one chart, so here come the charts!

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Counting Jobs

Jeremy Horpedahl2 Comments

Last week I wrote about the challenges of counting deaths. But surely in economics, we can count better, especially when it comes to something concrete like the number of people working. Right?

Maybe not. If you follow the economic data regularly, you’ll know that once per month, the Bureau of Labor Statistics releases data on the employment situation of the nation’s economy. And if you are familiar with this report, you will probably know that it is based on two separate surveys, one of businesses and one of households. And furthermore, it gives us two separate measures of employment, the number of people working for pay.

Joseph Politano has been tracking the employment situation reports, and he writes that the two measures of employment have “completely diverged since March of [2022], with the establishment survey showing payroll growth of nearly 2.7 million and the household survey showing employment growth of 12,000.” The surveys are tracking the labor market differently, so it’s not surprising that they won’t be exactly the same (they rarely are), but this sort of discrepancy is huge. Even accounting for most of the differences between the surveys, there is still a gap of about 2 million jobs.

Today, the BLS released yet another measure of employment, this one comes from the Business Employment Dynamics series. The BED is not released as quickly as the data in the employment situation report — the BED data released today is for the 2nd quarter of last year. But that’s because this data is much more comprehensive, and it’s actually the same data underlying the employment measure from businesses in the monthly employment report (it comes from unemployment insurance records, which covers most of the workforce).

What did the BED find for the 2nd quarter of 2022? A net loss of 287,000 jobs. The BED is only looking at private-sector jobs, and it is also seasonally adjusted to smooth out normal quarterly fluctuations. If we look back at the monthly data on employment, what did it look like in the 2nd quarter of 2022? Using the seasonally adjusted, private-sector jobs number to match the BED, it showed a gain of 1,045,000 jobs. In other words, we have a discrepancy of 1.3 million jobs in a single quarter. This is huge.

Perhaps some of this could be attributed to different seasonal adjustment factors, but even using the unadjusted data there is still a gap: 3,089,000 jobs added in the monthly payroll survey (private sector only), but only a net gain of 2,432,000 private-sector jobs in the BED data. That discrepancy is smaller, but it is still a difference of over 600,000 jobs. Note here that there was job growth in the second quarter in the BED measure, just not enough job growth that on a seasonally adjusted basis that it showed net growth. Another way to think of this: there is almost always growth in the 2nd quarter, but we expected it to be a bit stronger than this data shows.

If you aren’t confused enough yet, BLS produces yet another measure of employment, called the Quarterly Census of Employment and Wages. Really this is the broadest measure of jobs and is using the same underlying data as the BED and monthly nonfarm jobs in the business survey. But like the BED, it is also released with a significant lag. What does it show? A gain of 2,338,000 jobs in the 2nd quarter of last year (this includes public sector employment too). That number isn’t seasonally adjusted and compares with the CES (monthly nonfarm employment) number of 2,702,000, a discrepancy of 364,000 jobs (note: the CES will later be revised and benchmarked with the QCEW data).

What can we learn from all these different estimates of jobs? And which is right? The short answer to the second question is: they are all right, but measuring different things. The big takeaway is that there was indeed job growth in the 2nd quarter of 2022 (even the household survey shows job growth), but based on more complete data the monthly business survey probably overstated job growth, and it may have actually been pretty weak job growth compared to what we would normally expect in that quarter in the private sector (but of course, we aren’t in normal times).

If You Get Too Cold, I’ll Tax the Heat

Zachary BartschLeave a comment

Public utilities are funny things. The industry is highly capital intensive and many argue that it makes for natural monopolies. At the same time, access to electricity and water (and internet) are assumed as given in any modern building. Further, utility providers are highly, highly regulated at both the state and federal levels of government. Many utilities must ask permission prior to changing anything about their prices, capital, or even which services they offer.

Don’t get me wrong. Utility companies have a sweet deal. They are protected from competition, face relatively inelastic demand for their goods, and they have a very dependable rate of return. I just can’t help feeling like state governments are keeping hostage a large firm with immobile fixed business capital. For that matter, given what we know about the political desire for opaque taxation, I also have a suspicion that many states might tax their populations by using the utility companies as an ingenious foil. “Those utility companies are greedy, don’t you know. It’s a good thing that they are so highly regulated by the state.”  

There are two types of utility taxation. 1) Gross receipts taxes are like an income tax. From the end-user’s perspective, the tax increases with each unit consumed. 2) A utility license tax is like a fee that the utility must pay in order to operate in the state. From the user’s perspective, well… This tax may not even appear on the monthly bill. But if it does, then the tax per household falls with each additional household that the utility serves. Either way, state governments can get their share of the economic profits that protection affords. Below is map which shows the 2021 cumulative utility tax per resident in each state.

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On Counting and Overcounting Deaths

Jeremy Horpedahl2 Comments

How many people died in the US from heart diseases in 2019? The answer is harder than it might seem to pin down. Using a broad definition, such as “major cardiovascular diseases,” and including any deaths where this was listed on the death certificate, the number for 2019 is an astonishing 1.56 million deaths, according to the CDC. That number is astonishing because there were 2.85 million deaths in total in the US, so over half of deaths involved the heart or circulatory system, at least in some way that was important enough for a doctor to list it on the death certificate.

However, if you Google “heart disease deaths US 2019,” you get only 659,041 deaths. The source? Once again, the CDC! So, what’s going on here? To get to the smaller number, the CDC narrows the definition in two ways. First, instead of all “major cardiovascular diseases,” they limit it to diseases that are specifically about the heart. For example, cerebrovascular deaths (deaths involving blood flow in the brain) are not including in the lower CDC total. This first limitation gets us down to 1.28 million.

But the bigger reduction is when they limit the count to the underlying cause of death, “the disease or injury that initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury,” as opposed to other contributing causes. That’s how we cut the total in half from 1.28 million to 659,041 deaths.

We could further limit this to “Atherosclerotic heart disease,” a subset of heart disease deaths, but the largest single cause of deaths in the coding system that the CDC uses. There were 163,502 deaths of this kind in 2019, if you use the underlying cause of death only. But if we expand it to any listing of this disease on the death certificate, it doubles to 321,812 deaths. And now three categories of death are slightly larger in this “multiple cause of death” query, including a catch-all “Cardiac arrest, unspecified” category with 352,010 deaths in 2019.

So, what’s the right number? What’s the point of all this discussion? Here’s my question to you: did you ever hear of a debate about whether we were “overcounting” heart disease deaths in 2019? I don’t think I’ve ever heard of it. Probably there were occasional debates among the experts in this area, but never among the general public.

COVID-19 is different. The allegation of “overcounting” COVID deaths began almost right away in 2020, with prominent people claiming that the numbers being reported are basically useless because, for example, a fatal motorcycle death was briefly included in COVID death totals in Florida (people are still using this example!).

A more serious critique of COVID death counting was in a recent op-ed in the Washington Post. The argument here is serious and sober, and not trying to push a particular viewpoint as far as I can tell (contrast this with people pushing the motorcycle death story). Yet still the op-ed is almost totally lacking in data, especially on COVID deaths (there is some data on COVID hospitalizations).

But most of the data she is asking for in the op-ed is readily available. While we don’t have death totals for all individuals that tested positive for COVID-19 at some point, we do have the following data available on a weekly basis. First, we have the “surveillance data” on deaths that was released by states and aggregated by the CDC. These were “the numbers” that you probably saw constantly discussed, sometimes daily, in the media during the height of the pandemic waves. The second and third sources of COVID death data are similar to the heart disease data I discussed above, from the CDC WONDER database, separated by whether COVID was the underlying cause or whether it was one among several contributing causes (whether it was underlying or not).

Those three measures of COVID deaths are displayed in this chart:

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House Rich – House Poor

Zachary Bartsch3 Comments

Last week I presented a graphic that illustrates the changing average price of homes by state. This week, I want to illustrate something that is more relevant to affordability. FRED provides data on both median salary and average home prices by state. That means that we can create an affordability index. Consider the equation for nominal growth where i is the percent change in median salary (s), π is the percent change in home price (p), and r is the real percent change in the amount of the average home that the median salary can purchase (h).

(1+i)=(1+π)(1+r)

Indexing the home price and salary to 1 and substituting each the percent change equation (New/Old – 1) into each percent change variable allows us to solve for the current quantity of average housing that can be afforded with the median salary relative to the base period:

h=s/p-1

If h>0, then more of the average house can be purchased by the median salary – let’s vaguely call this housing affordability. Both series are available annually since 1984 through 2021 for all 50 states and the District of Columbia. The map below illustrates affordability across states. Blue reflects less affordable housing and green reflects more affordable housing since 1984.

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