Data

Answer: No, We Were Not in a Recession

Jeremy HorpedahlLeave a comment

About one year ago, I wrote a post with the title “Are We in A Recession?” At the time there was much talk, both in the popular media and among economists, about whether we were in a recession or not, and what “technically” counts as a recession. Now with hindsight, I think we can pretty clearly say that we were not in a recession last summer, nor at any point in 2022.

One thing is true: GDP did decline for two quarters in the first half of 2022. In fact, even the more nuanced “real average of GDP and GDI” declined for two quarters. But as I explained in that July 2022 post, that’s not how the NBER defines a recession. It often coincides with their defined recession, but they used a separate set of indicators. And while some economics textbooks do use the two quarters of declining GDP definition, as I explained in a follow-up post, that’s not the most common textbook definition.

The first half of 2022 is a good candidate for a possible recession, but when we look at the NBER’s preferred 6 measures of economic activity, it seems pretty clear that this was not a recession. If you start the data in the last few months of 2021, you do have small declines in two measures through July 2022 (real personal income and real manufacturing sales), but this looks nothing like past recessions, which have large declines in all or most of the 6 measures.

OK, but that was then, this is now. Are we in a recession now or headed into one? You can find lots of models and surveys or different groups of economists out there. I’m not sure that any particular one is the best, so I won’t dive into those. But if we look at the average of GDP and GDI again, we do notice that 2022q4 was negative and 2023q1 was very weak. Maybe that was a recession?

Again, we can start the NBER indicators around that time to see. Starting from September 2022, we can indeed see that there is some weakness in a lot of the measures for the next 2-3 months. But when we look out 6 months or so from then, we once again only have 2 of the 6 indicators that are below the September 2022 level, and the declines are mild (less than 1 percent). You can play around with the start date a bit, but I think September is the best candidate for a peak, and it’s still pretty weak.

OK, OK, you say, but that’s still all the past. What about the future? Sorry dear reader, I don’t have a crystal ball or the economic equivalent (a model). All I can say is what the data shows right now (which is always backward looking), and as of right now most broad measures of the economy aren’t declining. Yet!

This doesn’t mean everything is great in the economy. Inflation is bad. Poverty is bad. Inequality is, often, bad. We always have these things. But are they getting better? Or are they getting worse? A recession is a particularly bad thing, and something that is often hard to precisely define and measure (for good reason: the economy is complex and hard to measure!). All indication of the available data is that, whatever other bad things are happening right now, a recession is probably not one of those things.

Easy FRED Stata Data

Zachary Bartsch1 Comment

Lot’s of economists use FRED – that’s Federal Reserve Economic Data for the uninitiated. It’s super easy to use for basic queries, data transformations, graphs, and even maps. Downloading a single data series or even the same series for multiple geographic locations is also easy. But downloading distinct data series can be a hassle.

I’ve written previously about how the Excel add-on makes getting data more convenient. One of the problems with the Excel add-on is that locating the appropriate series can be difficult – I recommend using the FRED website to query data and then use the Excel add-on to obtain it. One major flaw is how the data is formatted in excel. A separate column of dates is downloaded for each series and the same dates aren’t aligned with one another. Further, re-downloading the data with small changes is almost impossible.

Only recently have I realized that there is an alternative that is better still! Stata has access to the FRED API and can import data sets directly in to its memory. There are no redundant date variables and the observations are all aligned by date.

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A Pessimistic Take on Inflation

Jeremy Horpedahl1 Comment

Last week I wrote an optimistic take on inflation. The rate of general price inflation has fallen a lot in recent months, and wage growth is now clearly outpacing inflation. That’s all good news.

Today, the Fed will announce their latest interest rate decision. Will the good news on inflation lead the Fed to stop raising interest rates? I’m not very good at making predictions, but today I’ll give a pessimistic take on inflation which suggests the Fed (and everyone else) should still be concerned about inflation.

The pessimistic take can be summarized in two charts. First, this chart shows the year-over-year change in the core PCE inflation index. As most readers will know, core indexes take out food and energy prices. This is not a “cheat” to mask important goods, it’s done because these are particularly volatile categories of goods. If we want to see the true underlying trend in inflation, we should ignore price fluctuations that are driven largely by weather and geopolitics.

While there is some moderation in inflation in this chart, we don’t see anything like the dramatic decline in the CPI-U, which fell from about 9 to 3 percent over roughly the past year. True, there is some decline over the past year, but only about 1 percentage point, and it has been stuck at just over 4.6 percent for the past 6 months. This is not a return to normalcy, as this rate historically has stayed in the band of 1-2 percent.

The second pessimistic chart is M2, a broad measure of the money supply.

The dramatic increase in M2 during 2020 is clear. That’s a big source of the inflation issues we’ve had over the past 2 years. There is some cause for optimism in this chart: M2 has clearly shrunk from the peak in Spring 2022. In fact, using a year-over-year percentage change, M2 has been negative since last November.

But if we look very recently, there is less cause for optimism. Since late April, M2 has stopped falling. In fact, it’s up a little bit. Is this a sign that the Fed doesn’t really have inflation under control? Perhaps. The increase isn’t huge, and there’s always some seasonality and noise to this data so we shouldn’t overanalyze this small deviation from the general decline in the past year plus. But we’ll need to continue watching this data.

Prohibition Reversals

Zachary BartschLeave a comment

We have all heard of the prohibition era. Popularly, it refers to the period from 1920-1933 during which it was illegal to sell, transport, and import alcohol in the US. National prohibition was enacted by the 18th amendment and repealed by the 21st amendment. That’s the basic picture.

But did you know that there were state alcohol prohibitions prior to the national one? In fact, there were 3 major waves of state alcohol prohibitions. The first was in the 1850s, the 2nd was in the 1880s, and then the 3rd preceded the 18th amendment. The image below illustrates the number of states that had statewide dry policies. You can see the first two waves and then the tsunami just prior to 1920.

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The Rate of Inflation is Falling, But Prices are Still Rising (And So are Wages)

Jeremy Horpedahl3 Comments

The latest CPI-U price data shows that the rate of inflation in the US has slowed significantly to just 3% in the past 12 months. That’s a huge improvement from the peak last June, when the annual rate of inflation was over 9%. Still, prices as a whole aren’t falling, and they clearly aren’t anywhere near where they were before the pandemic: using the CPI-U, prices are up over 17% since January 2020.

Lately I’ve heard many people asking a good question: will prices ever get back down to where they were? Usually they mean pre-pandemic prices, though sometimes they refer to a particular point-in-time (such as the start of Biden’s presidency). The only correct answer is “we don’t know,” but I think a likely answer for many goods and services is “no.” For many reasons, the nominal prices of most goods and services rise over time. Though this is not true for everything, of course (newer technologies are one example we often see).

But what about specific goods that we buy frequently? Will we ever see gasoline consistently below $3 per gallon again? Will we ever see milk consistently below $4 per gallon again? What about eggs and bread? And indeed, these prices are well above January 2020 levels: 23% higher for milk, 43% for bread, 45% for gasoline, and a whopping 52% for eggs. For the price data, I am using this convenient data on common food and energy goods from BLS.

For some of these items, I do think you might someday see prices fall back to levels consumers were used to from the recent past, since food and energy prices tend to be volatile. For others, though maybe not. But I think we as consumers can become overly focused on staples that we buy frequently and can easily recall the price in our heads. For example, while eggs, bread, and milk are items that we buy frequently (including being the staples of stocking up before a storm), in total these constitute just 0.6% of average consumer spending.

If instead of those 3 staples, your mind naturally anchors on produce prices, the trends look different: oranges are up 23%, but bananas are only up 10%, and tomatoes are, in fact, down 14% since January 2020. But again, these items are less than 0.5% of total consumer spending. Ideally, we shouldn’t anchor on any one subset of goods when doing a good analysis, even if it is natural for us to do so in our lives as consumers.

This is where the benefit of a price index, like the CPI-U, comes in.

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Updated GDP and Inflation Data for G7 Countries

Jeremy HorpedahlLeave a comment

As we prepare for the release of second quarter GDP data over the next few weeks, here is a chart showing cumulative GDP growth (inflation adjusted) and Price inflation for G7 countries. While inflation has been high everywhere (except for Japan), the US comes out looking very well relatively on GDP growth. That’s especially true compared to the UK and Germany, which have also had high price inflation, but have actually had negative economic growth since the end of 2019.

Replicating Research with Restricted Data

James Bailey3 Comments

If a scientific finding is really true and important, we should be able to reproduce it- different researchers can investigate and confirm it, rather than just taking one researcher at their word.

Economics has not traditionally been very good at this, but we’re moving in the right direction. It is becoming increasingly common for researchers to voluntarily post their data and code, as well as for journals (like the AEA journals) to require them to:

Source: This talk by Tim Errington

This has certainly been the trend with my own research; if you look at my first 10 papers (all published prior to 2018) I don’t currently share data for any of them, though I hope to go back and add it some day. But of my most recent 10 empirical papers, half share data.

This sharing allows other researchers to easily go back and check that the work is accurate. This could mean simply checking that it is “reproducable”, i.e., that running the original code on the original data produces the results that the authors said. Or it could mean the more ambitious “replicability”, i.e., you could tackle the same question with different data and still find basically the same answer. Economics does generally does well at reproducability when code is shared, but just ok at replication.

Of course, even when data and code are shared, you still need people to actually do the double-checking research; this is still relatively rare because it is harder to publish replications than original research. But more replication journals are opening, and there are now several projects funding replications. The trends are all in the right direction to establish real, robust findings, with one exception- the rise of restricted data.

Traditionally most economics research has been done using publicly available datasets like the Current Population Survey. But an increasing proportion, perhaps a majority of research at top journals, is now done using restricted datasets (there’s a great graph on this I can’t find but see section 3.3 here). These datasets legally can’t be shared publicly, either due to privacy concerns,licensing agreements, or both. But journals almost always still publish these articles and give them an exemption to the data sharing requirement. One the one hand it makes sense not to ignore this potentially valuable research when there are solid legal reasons the data can’t be shared. But it does mean we can’t be as confident that the data has been analyzed correctly, or that it even really exists.

One potential solution is to find people who have access to the same restricted dataset and have them do a replication study. This is what the Institute for Replication just started doing. They posted a list of 100+ papers that use restricted data that they would like to replicate. They are offering $5000 for replications of most of the papers, so I think it is worthwhile for academics to look and see if you already have access to relevant datasets, or if you study similar enough things that it is worth jumping through the hoops to get data access.

For everyone else, this is just one more reason not put too much trust in any one paper you read now, but to recognize that the field as a whole is getting better and more trustworthy over time. We will be more likely to catch the mistakes, purge the frauds, and put forward more robust results that at least bear a passing resemblance to what science can and should be.

The Cost of Raising a Child, Revisited

Jeremy Horpedahl1 Comment

Last week my post was about a new article I have with Scott Winship on the “cost of thriving” today versus 1985. That paper has gotten quite a bit of coverage, including in the Wall Street Journal, which is great but also means you are going to get some pushback. Much of it comes in the form of “it just doesn’t feel like the numbers are right” (see Alex Tabarrok on this point), and that was the conclusion to the WSJ piece too.

Here’s a response of that nature from Mish Talk: “There’s no way a single person is better off today, especially a single parent with two kids based on child tax credits that will not come close to meeting daycare needs.”

He mentions daycare costs, but never comes back to it in the post (it’s mostly about housing costs). Daycare costs are undoubtedly an important cost for families with young children (though since Cass’ COTI is about married couples with one earner, they may not be as relevant). And in the CPI-U, daycare and preschool costs only getting a weight of 0.5%. Surely that’s not reality for the families that actually do pay daycare costs! If only there was an index that applied to the costs of raising children.

In fact, there already is. Since 1960, the USDA has been keeping track of the cost of raising a child. Daycare costs are definitely given much more weight: 16% of the expenditures on children got to child care and education. And much of that USDA index (recently updated by Brookings) looks similar to what COTI includes: housing, food, transportation, health care, education, but also clothing and daycare. I wrote about it in a post last year and compared that cost to various measures of income (including single-earner families and median weekly earnings). But what if we compared it to Oren Cass’ preferred measure of income, males 25 and older working full-time? Here’s the chart.

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Wives Slightly Out-earning Husbands Is No Longer Weird

James BaileyLeave a comment

As we have gone through our education and training and changed jobs, my wife and I have been in every sort of relative income situation, with each one sometimes vastly or slightly out-earning the other. Currently she slightly out-earns me, which I thought was unusual, as I remembered this graph from Bertrand, Kamenica and Pan in the QJE 2015:

Ungated source: Bertrand Pan Kamenica 2013

The paper argues that the big jump down at 50% is driven by gender norms:

this pattern is best explained by gender identity norms, which induce an aversion to a situation where the wife earns more than her husband. We present evidence that this aversion also impacts marriage formation, the wife’s labor force participation, the wife’s income conditional on working, marriage satisfaction, likelihood of divorce, and the division of home production. Within marriage markets, when a randomly chosen woman becomes more likely to earn more than a randomly chosen man, marriage rates decline. In couples where the wife’s potential income is likely to exceed the husband’s, the wife is less likely to be in the labor force and earns less than her potential if she does work. In couples where the wife earns more than the husband, the wife spends more time on household chores; moreover, those couples are less satisfied with their marriage and are more likely to divorce.

But when I went to look up the paper to show my wife the figures, I found that the effect it highlights may no longer be so large.  Natalia Zinovyeva and Maryna Tverdostup show in their 2021 AEJ paper that the jump down in wives’ income at 50% is quite small, and is largely driven by couples who have the same industry and occupation:

They created the figure above using SIPP/SSA/IRS Completed Gold Standard Files, 1990–2004. I’d be interested in an analysis with more recent data. Much of their paper uses more detailed Finnish data to test the mechanism for the remaining jump down at 50%. They conclude that gender norms are not a major driver of the discontinuity:

We argue that the discontinuity to the right of 0.5 can emerge if some couples tend toward earnings equalization or convergence. To test this hypothesis, we exploit the rich employer-employee–linked data from Finland. We find overwhelming support in favor of the idea that the discontinuity is caused by earnings equalization in self-employed couples and earnings convergence among spouses working together. We show that the discontinuity is not generated by selective couple formation or separation and it arises only among self-employed and coworking couples, who account for 15 percent of the population.

Self-employed couples are responsible for most observations with spouses reporting identical earnings. When couples start being self-employed, both sides of the distribution tend to equalize earnings, perhaps because earnings equalization helps couples to reduce income tax payments, facilitate accounting, or avoid unnecessary within-family negotiations. Large spikes emerge not only at 0.5 but also at other round shares signaling the prevalence of ad hoc rules for entrepreneurial income sharing in couples. Self-employment is associated with a fall of household earnings below the level predicted by individuals’ predetermined characteristics, but this drop is mainly due to a decrease in male earnings, with women being relatively better off.

In the case of couples who work together in the same firm, there is a compression of the earnings distribution toward 0.5 both on the right and on the left of 0.5. As a result, there is an increase both in the share of couples where men slightly outearn their wives and in the share of couples where women slightly outearn their husbands. Since the former group is larger, earnings compression leads to a detection of a discontinuity.

So, concerns about relative earnings aren’t causing trouble for women in the labor market. But do they cause trouble at home? Perhaps yes, but if so its not in a gendered way and not driven by the 50% threshold:

Separation rates do not exhibit any discontinuity around the 0.5 threshold of relative earnings. Instead, the relationship between the probability of separation and the relative earnings distribution exhibits a U-shape, with higher separation rates among couples with large earnings differentials either in favor of the husband or in favor of the wife.

The American Family Is Thriving, Even if They Only Have One Male Earner (But Most Don’t)

Jeremy Horpedahl4 Comments

62 weeks. That’s how long the median male worker would need to work in a year to support a family in 2022, according to the calculations of Oren Cass for the American Compass Cost-of-Thriving Index released this year. Not only is 62 weeks longer than the baseline year of 1985 (when it took about 40 weeks, according to COTI), but there is a big problem: there aren’t 62 weeks in year. It is, by this calculation, impossible for a single male earner to support a family.

Is this true? In our new AEI paper, Scott Winship and I strongly disagree. First, we challenge the 62-week figure. With a few reasonable corrections to Cass’ COTI, we show that it is indeed possible for a median male earner to support a family. It takes 42 weeks, not 62 as reported in COTI.

But wait, there’s more. Much more. In our paper, we provide a range of reasonable estimates for how the cost of thriving has changed since 1985. In the COTI calculation, the standard of living for a single-earner family has fallen by 36 percent since 1985. In our most optimistic estimate, the standard of living has risen by 53 percent. The chart below summarizes our various alternative versions of COTI. How do we get such radically different results? Is this all a numbers game?

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