Some Stock Market Benchmarks

When I get the quarterly announcements for what my retirement account is now worth, and a drop in the stock market has caused the total in the account to decline, I find myself looking at some of the long-run patterns in stock market prices.

To set the stage, here’s the historical pattern of the S&P stock index since back in the 19th century. In interpreting the figure, notice that the vertical axis is a logarithmic axis showing proportional changes; for example, the tripling from 10 to 30 is the same size as the tripling from 100 to 300 and the same as the tripling from 1000 to 3000. (Without using a log scale, all the smaller values–like the stock market crash of 1929, would just be a little squiggle what would look like a nearly flat line on the far left of the figure.) You can see some of the well-known changes in the stock market over time: the run-up of the 1920s, the crash of 1929, the run-up of the 1960s, the comparatively flat market of the 1970s, a big jump in the dot-com market of the 1990s, the run-up since 2009, and the recent decline. Of course, whenever you consider the possibility that the

For a slightly different view, here is the same set of data, this time adjusted for inflation. Again, the vertical axis shows proportional change. Again, the main well-known changes are pretty visible, but they don’t all look the same. For example, after adjusting for inflation, the Black Tuesday stock market decline in the 1920s looks even more striking, and during the high-inflation 1970s, the real value of the S&P 500 index is falling.

Of course, stock market values should be affected by expectations of corporate earnings. Thus, the standard price-earnings measure of the stock market looks at stock prices divided by corporate earnings over the previous 12 months. Notice that the logarithmic scales have now gone away. Corporate earnings will rise over the long run both because of inflation and along with overall growth in the economy. Thus, one might expect to see the P/E ratio be roughly the same over time, of course with some fluctuations as economic events and market trends interact. Indeed, when you try to find the 1929 stock market crash in this data, it’s barely apparent: after all, if both stock prices and corporate earnings collapse at about the same time, then the ratio of the two may not move in an especially dramatic way.

You can also see that from the late 19th century up to about 1990, the P/E ratio didn’t seem to have a long-run trend. It moved up and down, but was typically in the range of about 15-20. Starting in the 1990s, however, the P/E ratio moved way out of its historical range, first during the dot-com boom of he 1990s, then in the run-up to the 2008-9 Great Recession. After its recent decline, the P/E ratio is now actually back in its historical range.

However, the P/E ratio has at least one substantial drawback: if corporate earnings rise or fall sharply in a given year, perhaps for idiosyncratic reasons, then the denominator of the P/E ratio will be jumping around. It might be useful to look at the average of corporate earnings for a time longer than 12 months, to smooth out this kind of short-run variation. Thus, what is called the “cyclically-adjusted price earnings ratio, also known as the CAPE ratio or the Shiller ratio, divides the value of the S&P 500 index by the average inflation-adjusted earnings of firms in the index over the prior 10 years. Here’s the CAPE ratio:

Notice that with this longer-term adjustment, Black Tuesday in 1929 becomes apparent again: essentially, the rise in stock prices in the 1920s and the fall in 1929 is now being compared not just to contemporary earnings, but to earnings over the previous 10 years. Something similar happens in the Great Recession of 2008-9: that is, the sharp decline in earnings drove the unadjusted P/E ratio in the previous figure sky-high, but when earnings are averaged over the previous decade, the CAPE ratio doesn’t show a similar peak. Again, there doesn’t seem to be much long-run trend in stock market prices up to about 1990, but then stock prices break out to historically high levels.

Recent movements in stock market prices look a little different in the context of CAPE ratio. The jump in 2021 looks smaller than with the regular P/E ratio, and the recent decline also looks smaller. The difference arises because corporate earnings have grown rapidly in the last five years or so. This growth in earning tended to hold down the plain vanilla P/E ratio up to 2021–because the S&P 500 was rising with these earnings–but with longer-term average earnings taken into account the rise in stock market prices pre-2021 becomes more apparent. By the CAPE metric, stock prices after their recent decline are still at high although not historically unprecedented levels,

Will the stock market stay at this higher CAPE level? I’m definitely not in the prediction business, and even more definitively not in the short-term prediction business. But Eric Zitzewitz, without making a prediction himself, summarizes some of the key issues in one of the always-useful briefs from the Econofact website:

There are reasons that a higher-than-normal CAPE might be appropriate in 2022. First, interest rates are lower than inflation, so the real interest rate (the nominal rate minus the inflation rate) is negative. This should increase the amount an investor should be willing to pay for $1 of current earnings since earnings (and the dividends they fund) should grow with inflation while the alternative of holding a bond rather than a stock is less attractive because of still low nominal interest rates. Second, high price-earnings ratios in the United States partly reflect a sectoral sorting between the United States and Europe in which the United States has more high-growth technology companies and Europe more slower-growing consumer goods corporations. Third, CAPE, by design, underreacts to recent earnings growth. Earnings have grown very rapidly in the last decade: After not growing at all per unit of the S&P 500 between 2006 and 2016, earnings have almost doubled in 5 years, adjusted for inflation. When attempting to predict future performance using past performance, it is sensible to put more weight on more recent performance. CAPE does not do this; in benchmarking value against equal-weighted average prior earnings, it implicitly takes the 10-year average as its prediction of future earnings. Valuations in 2021 look less like an outlier if we benchmark them with prior-year earnings, as in the simple P/E ratio.

Here one final benchmark, known as the Buffett ratio, because Warren Buffett said back in 2001 in a Fortune Magazine interview that “it is probably the best single measure of where valuations stand at any given moment.” It’s the ratio of all publicly traded stocks (as measured by the Wilshire 5000 index) to the US gross domestic product.

From 1970 to the present, the average value of the Buffett ratio is a little under 90%: that is, the ratio of the value of all publicly traded stocks to GDP was a little under 90%. When Buffett made his comment in 2001, you could have made money expecting the ratio to decline: indeed, you could have also made money on this advice up to the period before the Great Recession. But the run-up in stock prices since 2009 took stock prices to about 200% of GDP before the recent decline–and the value still sits at about 170% of GDP. As Zitzewitz notes, your confidence in this stock market value will depend on the extent to which you think recent increases in corporate earnings are likely to be lasting, as well as on how you evaluate the “sectoral sorting” of high growth companies into the US economy.

Of course, whenever you are contemplating that perhaps the stock market is behaving irrationally and that perhaps you can make money betting that it will return to its “true” value, it is worth remembering the uncertainties involved in such judgements, and in particular, to remember the old investment aphorism that “the market can remain irrational longer than you can remain solvent.”

CAHOOTS in Denver: Experimenting with Alternatives to Police

A particular challenge for police is that they often end up as the first responders to all sorts of emergency calls, not just those related to an obvious crime. In some cases, might there be a more effective alternative?

In a paper in the Fall 2021 Journal of Economic Perspectives, Monica Bell makes the case that academic research on policing has focused heavily on the number of police and on methods of deploying police, but much less on alternatives to a police response or on alternative ways of reducing crime (“Next-Generation Policing Research: Three Propositions,” 35:4, 29-48). Here’s one of the examples that she mentions:

But there are a number of other community-based programs or alternatives to traditional policing that remain largely unstudied, even though some of them are becoming models for other jurisdictions across the nation. For example, CAHOOTS (Crisis Assistance Helping Out on the Street) started in Eugene, Oregon, to send two-person clinical response teams to aid people in mental health crisis, without relying on armed police officers. Although the program has existed for more than three decades, in summer 2020 it gained national attention and became the model for numerous pilot programs—in San Francisco, Denver, Rochester, Toronto, and more. Eugene’s CAHOOTS program is funded and overseen by the police department, but some other emerging programs are funded and managed separately from police. Despite its long duration—even longer than the violence interruption programs mentioned above—CAHOOTS has never been rigorously evaluated. There are also rich debates over, among other things, how to measure its diversion rate (Gerety 2020). There is a dearth of information and modeling of police-free crisis response, though one hopes that will change as more cities embrace these approaches.

Well, now there is at least one piece of evidence on a CAHOOTS-style program. Thomas Dee and Jaymes Pyne write about the Denver experience in “A community response approach to mental health and substance abuse crises reduced crime” (Science Advances, June 8, 2022. 8: 23). Here’s some background (citations omitted):

Support Team Assistance Response (STAR) program in Denver provides a mobile crisis response for community members experiencing problems related to mental health, depression, poverty, homelessness, and/or substance abuse issues. The STAR response consists of two health care staff (i.e., a mental health clinician and a paramedic in a specially equipped van) who provide rapid, on-site support to individuals in crisis and direct them to further appropriate care including requesting police involvement, if necessary. The design of the STAR program is based on the Crisis Assistance Helping Out On The Streets program developed in Eugene, Oregon.

STAR began operations on 1 June 2020 for a designated 6-month pilot period. During this period, STAR limited its operations to selected 911 calls for assistance in eight purposefully chosen police precincts (i.e., out of the city’s 36 precincts), where the need for STAR services was anticipated to be the greatest. … [A]ll but one of the neighborhoods in the STAR pilot service area are also designated by the city as “displacement-vulnerable” areas, rapidly gentrifying city spaces where poor and otherwise at-risk residents are being pushed out. …

Operators responding to 911 calls for assistance dispatched STAR staff to eligible incidents that were located in the designated police precincts and during the program’s hours of operation (Monday to Friday, 10 a.m. to 6 p.m.). The identification of emergency calls eligible for STAR services relied on two specific screening criteria. First, the incident had to designate at least one of several codes: calls for assistance, intoxication, suicidal series, welfare checks, indecent exposure, trespass of an unwanted person, and syringe disposal. Second, to dispatch the STAR van, there needed to be no evidence that the incident involved serious criminal activity, such as weapons, threats, or violence, or serious medical needs. The STAR team also responded to calls from uniformed police to engage with community members in crisis and initiated engagement in the field on their own. Over the 6-month pilot period, the STAR team responded to 748 incidents or nearly 6 incidents per 8-hour shift. Roughly a third of calls to STAR occurred at the request of responding police, while the rest were due to a direct 911 dispatch or to the STAR team responding independently to a field observation—none of which required a call to police for assistance or for a response to a criminal offense.

Because the program was rolled out only in certain precincts, and only at certain times and days of the week, it becomes possible to compare trends and patterns, looking both at patterns within the precincts and in comparison with the contiguous areas. (Those who want details on how these “difference-in-differences” comparisons are done can scan through the paper itself.) In particular, the authors looked at certain low-level crimes that seemed likely to be interrelated with the kinds of situations to which the STAR team was responding, like disorderly conduct, trespassing, alcohol, and drug use. Again, the STAR team was not being dispatched for serious crimes or medical emergencies.

The authors find that “the service reduced the number of STAR-related offenses in treated precincts by 34% over the 6 months of the pilot phase. … This impact estimate implies that the STAR pilot program prevented nearly 1400 criminal offenses within the eight participating precincts and the 6 months of operation … This program-induced reduction in measured offenses is broadly consistent with the scale of STAR operations. Specifically, the STAR team responded to 748 calls during our study window. At baseline (i.e., during the pretreatment period), each STAR-related incident resulted in an average of 1.4 recorded offenses in treated precincts. This suggests that we should expect 748 field calls by STAR staff to result directly in just over 1000 fewer recorded offenses (i.e., 748 × 1.4 = 1047).”

It’s interesting to note that some of the decline in the number of criminal offenses happened outside the 10-6 timeframe when the STAR program was actually operating, which is consistent with the idea that the STAR interventions didn’t just deal with the immediate issue, but improved the broad situation. Moreover, this approach seems potentially cost-effective:

The total cost of the 6-month STAR pilot program was $208,141 (50). One useful way to frame this public outlay is to note that the corresponding reduction of 1376 offenses implies a cost of $151 per offense reduced. To put this in perspective, the available estimates (8) suggest that the direct criminal justice cost for a minor criminal offense (e.g., imprisonment and prosecuting) averages $646 (in 2021 dollars). In other words, the direct costs of having police as the first responders to individuals in mental health and substance abuse crises are over four times as large as those associated with a community response model. A fuller reckoning of the costs and benefits associated with community response models would also include the costs and benefits associated with any health care brokered by the first responders. For example, police officers may be more likely than community responders to direct individuals in crisis to comparatively expensive emergency room care or to no care at all. Nonetheless, the results presented here suggest that community response models merit careful consideration as a highly cost-effective way to reduce police engagement with nonviolent individuals in crisis and to instead respond with appropriate health care.

Of course, the study has some obvious limitations. For example, one would want to be hesitant about extrapolating from a program that operated from 10-6 on weekdays to what might happen late on a Friday or a Saturday night. In addition, the focus on number of low-level crimes reported is interesting, but surely ways incomplete. Yes, it’s good for people not to build up a record of low-level criminal offences. But was the problem that led to the STAR team being called resolved in a way that helped the people directly involved? Did other community members who were affected feel that the problem had been addressed?

With these and other caveats duly noted, the partial results are clearly encouraging. As the authors write:

Nonetheless, the results presented here suggest that community response models merit careful consideration as a highly cost-effective way to reduce police engagement with nonviolent individuals in crisis and to instead respond with appropriate health care.

Human Capital: Your Nonfinancial Wealth

Most people are used to thinking about their wealth in purely financial terms. But consider the situation of someone who has just graduated from law school or medical school and is about to head into a high-paying job. Financial wealth for this person may be low–or even negative if there are student loans to be paid. But they do have an enormous non-financial asset, namely the skills and credential that will allow them to earn a high salary in the future.

Or compare two 21 year-olds, one with only a high school degree and one who has completed a college degree. Their financial wealth may be similar: again, if the college student has loans outstanding, the financial wealth of the college student may be lower. But on average, a college graduate has a nonfinancial asset that will allow them to earn a higher future salary.

Economists refer to this kind of nonfinancial personal asset as “human capital.” The idea behind the terminology is to he idea was to draw a parallel with physical capital. In each case, an economic actor incurs costs in the present that over time have a long-run payoff. With human capital, the costs can involve both money (say, college tuition) and also time (when you go beyond what’s strictly needed to do today’s job and acquire skills that will be applicable in the future). A broad view of human capital can include not just education and job experience, but also physical and mental health.

A team of researchers at McKinsey Global Institute have written a report focusing on one aspect of the topic: “Human capital at work: The value of experience (June 2, 2022). The McKinsey team looked at “a data set of de-identified job histories for approximately four million workers across the United States, Germany, the United Kingdom, and India.”

They find that for those early in a career, entry-level skills from the education system are a main determinant of wages. But over time, job experience is a bigger and bigger part of your human capital.

Here’s an illustration for the US workers in their data, over a 30-year working life. Salary rises over the lifetime. After 10 years, 61% of pay can be attributed to entry-level skills (from formal education), but after 30 years, 60% of pay can be attributed to work experience. Notice also that the pay raises over time are because of the rising role of work experience.

Of course, these results are an overall average. The report gives example of how the pay premium for higher experience (in a US context) is very large for some jobs like airline pilots and physicians, but not so large for jobs like maintenance and repair workers.

Not all job experience is created equal. People who switch to a different job with a different employer, in a way that stretches and expands their skills, will expand their experience-related human capital. This insight has implications both for workers and employers. For workers, the report offers these categories:

From our data set, we looked at a smaller universe of people with more than ten years of work history. Within it, four distinct archetypes emerge. They are not meant to convey individuals’ circumstances or motivation; they describe movement patterns and outcomes, with illustrative examples.
— Experience seekers start with lower-than-average wages but propel themselves upward by moving roles more frequently than their peers and stretching their capabilities substantially each time. The cumulative effect gives them stronger wage growth than any other archetype. Consider someone who starts as an administrative assistant at one nonprofit before landing a job cultivating donors in the development department of
another. From there, she joins a research hospital as a grant writer before stepping into
a broader communications role. Eventually she becomes head of media relations for a
major university. Our experience seeker has managed to cross over into new industries
and functions.
— Early movers make bigger leaps in the first part of their career. Someone may start in one field, quickly realize that their passion lies elsewhere, and then get a break that enables them to follow it. A graphic designer who makes print ads, for example, might become a user-experience designer early in her career.
— Late movers stay put or make more incremental moves in the early stage of their career but eventually take a bolder step. Think of a seasoned journalist who goes into corporate communications, or a real estate agent who becomes a mortgage loan officer in a bank. This is by far the largest group in the sample.
— Lock-ins change jobs less frequently, and when they do move, they do not make dramatic changes. This is not necessarily because someone is timid or stuck; they could also follow this strategy because they pursued what suited them from the start. Teachers,
for example, have invested in specialized education and may have found their calling.
However, lock-ins have the slowest wage growth, whether they start near the bottom or
near the top. Doctors start at a very high salary but do not tend to make many role moves.
While work experience accounts for 60 to 70 percent of lifetime earnings for experience
seekers and early movers, that share is only about 30 percent for lock-ins.

There are implications for employers looking for talent, as well.

Most employers can benefit from challenging the status quo of how they select people for open roles. Instead of searching for “holy grail” external candidates whose prior experience precisely matches the responsibilities in an open role, leading organizations create systems for evaluating candidates based on their capacity to learn, their intrinsic capabilities, and their transferable skills. This requires designing assessments that are fit for purpose, focusing on the few core skills that matter for success in the role. It also involves removing biases that pigeonhole people into the roles they are already performing; this point is particularly important when it comes to existing employees. In our sample, more than half of all role moves undertaken by individuals involved a skill distance of more than 25 percent—and this implies that people often have latent capabilities that are not recognized by their current employers. If someone’s track record shows the acquisition of new skills over time, it probably means that person is capable of learning more. Employers should be less constrained about recruiting candidates from traditional sources and backgrounds, and more open to people who have taken unconventional career paths.

I write as someone who has had the same job title, with the same employer, for 36 years. In the categories given above, I’m a lock-in. I’ve been very happy with my job and what I do. But especially for those early in their careers, thinking in a serious way about whether your current employer is helping to develop the breadth and depth of your work experience human capital–or whether a job with an alternative employer might help you to do so–is likely to be at least as important to your lifetime financial well-being as the decisions you make about financial savings and investment.

War in Ukraine and the Global Economy

The most recent OECD Economic Outlook report (June 2022) leads off with a discussion of how Russia’s attack on Ukraine is affecting the global economy. The first chapter begins like this (references to tables omitted):

The war in Ukraine has generated a major humanitarian crisis affecting millions of people. The associated economic shocks, and their impact on global commodity, trade and financial markets, will also have a material impact on economic outcomes and livelihoods. Prior to the outbreak of the war the outlook appeared broadly favourable over 2022-23, with growth and inflation returning to normality as the COVID-19 pandemic and supply-side constraints waned. The invasion of Ukraine, along with shutdowns in major cities and ports in China due to the zero-COVID policy, has generated a new set of adverse shocks. Global GDP growth is now projected to slow sharply this year to 3%, around 1½ percentage points weaker than projected in the December 2021 OECD Economic Outlook, and to remain at a similar subdued pace in 2023. In part, this reflects deep downturns in Russia and Ukraine, but growth is set to be considerably weaker than expected in most economies, especially in Europe, where an embargo on oil and coal imports from Russia is incorporated in the projections for 2023. Commodity prices have risen substantially, reflecting the importance of supply from Russia and Ukraine in many markets, adding to inflationary pressures and hitting real incomes and spending, particularly for the most vulnerable households. In many emerging-market economies the risks of food shortages are high given the reliance on agricultural exports from Russia and Ukraine. Supply-side pressures have also intensified as a result of the conflict, as well as the shutdowns in China. Consumer price inflation is projected to remain elevated, averaging around 5½ per cent in the major advanced economies in 2022, and 8½ per cent in the OECD as a whole, before receding in 2023 as supply-chain and commodity price pressures wane and the impact of tighter monetary conditions begins to be felt. Core inflation, though slowing, is nonetheless projected to remain at or above medium-term objectives in many major economies at the end of 2023.

The uncertainty around this outlook is high, and there are a number of prominent risks. The effects of the war in Ukraine may be even greater than assumed, for example because of an abrupt Europe-wide interruption of flows of gas from Russia, further increases in commodity prices, or stronger disruptions to global supply chains. Inflationary pressures could also prove stronger than expected, with risks that higher inflation expectations move away from central bank objectives and become reflected in faster wage growth amidst tight labour markets. Sharp increases in policy interest rates could also slow growth by more than projected. Financial markets have so far adjusted smoothly to tighter global financial conditions, but there are significant potential vulnerabilities from high debt levels and elevated asset prices.

While the report discusses a number of issues, two immediate concerns are commodity prices and refugees.

The major influence of Russia and Ukraine on the global economy is via their role as important suppliers in a number of commodity markets. Together they account for about 30% of global exports of wheat, 15% for corn, 20% for mineral fertilisers and natural gas, and 11% for oil. In addition, global supply chains are dependent on Russian and Ukrainian exports of metals (see below) and inert gases. The prices of many of these commodities increased sharply after the onset of the war, even in the immediate absence of any significant disruption to production or export volumes (Figure 1.1). … A particular concern is that a cessation of wheat exports from Russia and Ukraine could result in serious food shortages in many developing economies. There would be an acute risk not only of economic crises in some countries but also humanitarian disasters, with a sharp increase in poverty and hunger. The food supply shock could be compounded by fertiliser shortages and price rises, with Russia and Belarus major suppliers in many countries, putting agricultural output next year and perhaps beyond under stress.

On the magnitude of the refugees from Ukraine:

The war in Ukraine has generated a historic outflow of people fleeing the conflict, unseen in Europe since World War II. The Syrian conflict raged for two years before the number of refugees abroad reached three million in 2015-16, whilst this number was reached in less than 3 weeks for the war in Ukraine. By May 18, according to data from the UNHCR, more than 6.2 million people had fled Ukraine and an additional estimated 8 million were internally displaced. About 5.3 million Ukrainian refugees have reached the European Union. Close to 3.4 million Ukrainians crossed into Poland, almost 930 000 into Romania, 615 000 into Hungary and 427 000 into the Slovak Republic.

Over the medium-term, expect other consequences to arise: for example, from detaching European energy markets from Russian suppliers, from other shifts in global supply chains, and from costs of and reactions to international financial sanctions.

More Job Openings Than Jobs

For many people, the higher level of price inflation–and the reality that wages are not keeping up–is probably the most pressing economic concern of the day. But it’s worth paying some attention to the unusual situation in labor markets, where the number of job openings is higher than the number of unemployed people. Arthak Adhikari and Tamara Mickle of the US Bureau of Labor Statistics lay out some basic facts in “What is the unemployed people per job openings ratio? A 21-year case study into unemployment trends” (Beyond the Numbers, June 2022).

The dark blue line shows the number of unemployed people. The light blue line shows the number of job openings. The red line shows the gap between them. Notice that the red line is in negative territory: that is, the light blue line of job openings is higher than the dark blue line of unemployed people.

One can also look at these numbers as a ratio. This figure shows the ratio of unemployed people per job opening. You can see the rise immediately after each recession. The ratio is now below 1: again, the number of unemployed people is less than the number of job openings.

What’s striking to me is that this pattern of more job openings than unemployed is not just a post-pandemic phenomenon. It was part of a trend going back to the period after the Great Recession and up through early 2020, as well. Notice in the first figure that the number of job openings is steadily on the rise in the time before the pandemic–and recently as well–but the number of unemployed workers is falling. With all these job openings, why isn’t the unemployment rate even lower than 3.6%? Why do so many people seem to be finding it hard to get a job? Here are some of the (potentially overlapping) theories:

1) A mismatch theory would suggest that there are lots of jobs, but unemployed workers may not be qualified. If many of the jobs are for computer programmers, for example, then those without the desired skills aren’t going to get hired. International evidence from the IMF suggests that this is only a modest contributor to these patterns.

2) The Great Resignation theory suggests that there may be lots of jobs, but many of them don’t offer much in the way of pay, benefits, or career path. Thus, people who had those jobs in the past, or people who on the margin between working or not working, now have a greater tendency to opt out.

3) A pandemic hangover theory suggests that workers with health-related concerns may be especially slow to look for jobs. Similarly, after two years of disruption in school and child-care arrangements, a number of parents may be slow to look for jobs.

4) A transactions costs theory suggests that something about the nature of job search has changed. The old days of showing up at a store and filling out a job application in response to a help-wanted sign are, in many places, long-gone. Instead, firms post even entry-level jobs at their websites, and applying for a job can mean filling out pages of forms, uploading a resume, even an automated video interview. Firms have lots of reasons to have shifted their hiring in this way. But my sense is that the barriers of applying for a job in this way are real, perhaps especially for low-skilled workers. Workers must often plan to go through this application process at a number of potential employers, knowing that dozens or hundreds of other online applicants are doing the same thing. It may be that what a firm sees as a “job opening” is not the same as it used to be.

5) The US workforce is aging. The front edge of the post-World War II “Baby Boom” generation was born in 1945. That group hit age 65, and thus started retiring in force, around 2010. My sense is that a substantial number of older workers might be tempted to work, by the right mix of wages and job conditions, but don’t necessarily feel that they need to be pounding the internet (what we used to call “pounding the pavement”) looking for a job.

The underlying pattern here, at least up to the time before the pandemic recession, was a pair of countervailing trends in the labor force participation rate and the employment/population ratio. The falling labor force participation rate meant that a smaller share of adults were either employed or without a job but unemployed and looking for one. The rising employment/population ratio meant that out of the adult population, a larger share held jobs than before. Even before the pandemic recession, these patterns meant that firms were often seeking to hire people who didn’t count as officially unemployed, because they were out of the labor force and not actually looking for a job. This pattern of more job openings than unemployed existed before the pandemic. Except for periods of recession, it may be a new normal.

Interview with Tyler Cowen: Co-authorship, Causality, and More

David A. Price serves as interlocutor for an interview: “Tyler Cowen: On credentialism, the new math of causation, and the lasting economic influence of youthful experiences” (Econ Focus: Federal Reserve Bank of Richmond, Second Quarter 2022). There are a wide range of topics, accompanied by thought-provoking insights. Here are a few:

Why are Americans moving less over the past few decades?

I think there are at least two major developments behind that change. The first is that we’re just much more of a service economy and continue to become more and more services based. Say you’re a dentist. You don’t really think, “Well, I’ll move from Dallas to Denver, because Denver is where the teeth are.” Right? That wouldn’t make sense. In services, for the most part, you just pick where you want to live and you can stay there just fine. …

I think the other factor is that people are just better informed, partly because of the internet. They can figure out where they want to live earlier in life and then just stay there. Staying put comes with definite upsides. But it’s also the case when a downturn comes, maybe your labor markets don’t adjust the way they used to because everywhere is stuck in the same predicament. If everywhere looks a bit like Columbus, Ohio, or for that matter, Richmond, Virginia, there’s less moving.

Qualms about co-authorship and consensus in economics

I think macro has become underrated. One good thing about macro is that it’s not obsessed with co-authored papers. Co-authored papers are fine; they’re often necessary. Yet there’s something inertial or status-quo-prejudiced with a co-authored paper. Everyone does have to agree, right? In macro, single-authored papers may also be in decline, but they’re still relatively more common than in micro. And there’s something more revolution-friendly about that. Einstein didn’t co-author the general theory of relativity.

One of my concerns about economics is we’re too consensus-oriented at the refereeing stage, at the editing stage, and even at the co-authoring stage. Again, I don’t know how to say co-authoring doesn’t make sense. Papers are harder to do than before, and you need all these different skills. But I think it’s a problem we should talk about more.

“We’re at a point where you can often believe the result of a paper.”

Several decades ago, a lot of econometrics papers were based on running correlations in various ways and of varying degrees of complexity. Then there’d be some part of the paper later on where you’d wave your hands and tell a story about causation or make some remarks about what you might do someday to address causation. But a lot of what was there was actually fairly lame. You didn’t really know what was causing what and things were taken on faith, or you would refer to your theoretical framework. You’d think things like, well, I’m a monetarist or I believe in rational expectations, so I’m going to superimpose this story on the data. A lot of the macro of the 1980s and even the 1990s was like that.

If you try to do that today in papers, maybe you can still publish them in a lesser journal, but they don’t become influential papers. You need to set things up in a way that you’re actually attempting to see which variable is causing which variable. You do difference-in-differences, for instance, and you see that minimum wage laws were imposed first on these counties before other counties, and you look at the differential effect that that had. It’s not quite proof of causality, but it’s way better than what we used to do. We’re at the point where you can often believe the result of the paper. That’s pretty good; not too long ago, we weren’t at that point. That, in a way, is a little scary.

Africa: Tackling Some Big Economic Questions

Economic development typically involves a group of transitions, like the shift from agriculture to manufacturing to services. The Winter 2022 issue of the Journal of Economic Perspectives (where I work as Managing Editor) includes five papers on aspects of development-related transitions in the nations of Africa. Here are some of the big questions:

Does Africa have a manufacturing path to economic development?

In “Labor Productivity Growth and Industrialization in Africa,” Margaret McMillan and Albert Zeufack investigate Africa’s manufacturing sector. As they point out, a shift from agriculture to low-skilled manufacturing to high-skilled manufacturing to services has been a standard pattern of economic development for countries around the world. However, there are concerns that this path may not work well in the 21st century, because automated production keeps getting cheaper and thus reducing the opportunities for low-skilled jobs.

Some of the signs for industrialization in Africa are encouraging. The most
comprehensive information about manufacturing employment in Africa only covers
18 countries, but based on those data, manufacturing employment in Africa’s lowand middle-income countries increased from 6 million to more than 20 million from
2000 to 2018, raising the share of employment in manufacturing from 7.2 percent
to 8.4 percent (Kruse et al. 2021). In comparison, the 1990s saw zero growth in
Africa’s manufacturing employment. Manufacturing exports from African nations
have also grown at an annual average of 9.5 percent per year (Signé 2018). However,
while employment and value-added shares of manufacturing in Africa are rising,
both remain very low in comparison to the rest of the world …

But when the authors dig into the data, they find that the growth in manufacturing employment in nations of Africa has been primarily happening in small firms with less than 10 employees. Conversely, the growth in productivity in manufacturing in Africa is primarily in large firms, which aren’t adding many jobs. Many of Africa’s large manufacturing firms are in one way or another involves in processing of natural resources, which has been becoming an ever-more automation-intensive process.

Thus, the broad challenge for Africa’s manufacturing sector is for the larger firms to build linkages backward and forward into other African-based manufacturing firms, and for at least some of the small firms to make productivity gains and grow in size, so that they can become an “in-between” sector of manufacturing. One promising change is the African Continental Free Trade Area, started in 2018, which may offer possibilities for African-based manufacturing firms to sell and compete within a larger and more unified market. In addition, there are still some industries like certain kinds of textile manufacturing where low-wage labor can offer a comparative advantage in global production.

One common concern about manufacturing in Africa is also hard to wrap your hands around–the idea that there is a poor “business environment” in many nations. The authors point out that the relevant business environment comparison for many nations in Africa is countries like Bangladesh or Vietnam, and when you look at issues like transportation links or business conditions, a number of African nations do just fine in this comparison.

Much has been made of the poor business environment in Africa and business environment does matter, of course. But as nations across Asia have shown, where there are profits to be made, businesses find a way to work around business environment problems. Similarly, despite the business environment in Africa, formal manufacturing firms have performed well in terms of productivity growth (Diao et al. 2021). Indeed, measuring the business environment by the World Bank Doing Business index, many countries of Africa compare favorably to countries of Asia that have experienced rapid growth. In 2013, for example, Ghana ranked 27 countries ahead of Vietnam in the Doing Business indicators. … A comparison between the rankings of countries in Africa and those of countries in Asia with established bases in manufacturing for the year 2019 offers several similar examples. Rwanda ranks 40 points ahead of Vietnam at 29, Mauritius and Kenya are also ranked ahead of Vietnam at 21 and 61 respectively. Seventeen African countries rank ahead of Cambodia. Bangladesh has five million garment workers (ILO 2020), but out of 48 countries in Africa only eight countries are ranked below Bangladesh and seven of these countries are at war. Nigeria is ranked 30 points ahead of Bangladesh.

Can productivity growth in Africa’s agricultural sector push development forward?

The transition from agriculture to manufacturing involves both push and pull: increased productivity in agriculture freeing up workers to move to manufacturing, along with the pull of higher-paid manufacturing jobs. Tavneet Suri and Christopher Udry take on the topic of “Agricultural Technology in Africa.” As they describe it, agricultural technology in Africa is stagnating.

[A]griculture is almost 20 percent of GDP in Africa, compared with a world average of about 5 percent. Moreover, the share of agriculture in GDP of the African region has remained stable over the last 50 years, whereas the share for other regions that started high in 1970—South East Asia and South Asia—has fallen a lot. … [A]gricultural shares of employment have declined across regions of the world in the last 30 years. Africa now has the highest share of employment in agriculture at about 50 percent, given the declines
in the South Asia region, while the world average of employment in agriculture is
closer to 30 percent. … A first step towards structural transformation happens as the agricultural sector evolves from smallholder farmers growing mainly food crops (cereals) for self-consumption to larger scale farmers growing food crops primarily for sale. At present, about 80 percent of African farmers are smallholders with under two
hectares of land, who together account for 40 percent of cultivated area …

Standard improvements in agricultural technology include types of irrigation, fertilizer, pesticides, mechanization, improved seeds, and access to markets. In their overview of the literature, there’s no single cause of these issues in Africa. The successful smaller-scale programs seem to involve a mix of training, credit, access to inputs, crop insurance, and market access. Scaling all of these up at once is a real task.

In addition, the authors emphasize that modern agricultural production is very responsive to specific conditions of the land and the terrain. It can make sense for neighboring farmers, or with a single farm, to use very different mixtures of crops and technology mixes. One role of agricultural R&D is to figure out how to get these ideal mixtures. The authors write that in India, “it is common to have 20–40 new varieties of rice released each year since 1970, along with 10–20 new varieties of both maize and wheat each year”–and the number of new varieties is rising over time. Similar patterns are not happening in much of African agriculture.

How will Africa’s women in workforce interact with economic change?

Women entering the (paid) workforce is a common signal of economic development: for example, it alters the incentives female education and for fertility. Taryn Dinkelman and L. Rachel Ngai discuss “Time Use and Gender in Africa in Times of Structural Transformation.” From the Appendix:

We highlight two stylized facts about women’s time use in Africa. First, in North Africa, women spend very few hours in market work and female labor force participation overall is extremely low. Second, although extensive margin participation of women is high in sub-Saharan Africa, women tend to work in the market for only a few hours each week, with the rest of their work hours spent in home production. These two facts suggest two different types of constraints that could slow down the reallocation of female time from home to market as economies grow: social norms related to women’s market work, and a lack of infrastructure (e.g., household infrastructure and childcare facilities) to facilitate marketizing home production.

The authors collect time use survey data to make a number of intriguing comparisons:

African housewives do not work significantly more hours per week in the home than do American housewives. In fact, in some countries, African housewives work fewer hours. In the United States in 2010, housewives spent on average 45.7 hours per week in home production: about the same amount of time spent by Moroccan (45.7 hours), Ghanaian (45.8 hours), and (2010) South African (45.7) housewives. Only Sierra Leonean and South African housewives in 2000 report more hours in home production than American housewives in 2010. …

For most African housewives, the bulk of their time is spent cooking, cleaning, and doing laundry. In South Africa, Ghana, and Morocco, cooking absorbs between one-third to just over one-half of all home production hours. Cleaning takes another 6–27 percent of home production time, while laundry takes 5–13 percent. In contrast, child- and elder-care take at most 21 percent of hours, and higher-skilled household management takes at most 15 percent of home production time. … The composition of home hours among modern US housewives is the exact reverse of South African, Ghanaian, and Moroccan housewives. Over half of home production hours in 2010 are spent in home management and in care work, with only 15 percent of hours spent cooking, 20 percent cleaning, and 8 percent doing the dreaded laundry. … Housewives in South Africa, Ghana, Morocco spend their time much more along the lines of American women in the 1920s and the 1960s.

Can the economies of Africa created the jobs needed for young adult workers?

With populations expanding in many African countries, a key question for economic and social stability is what jobs will be available for young adults. Oriana Bandiera, Ahmed Elsayed, Andrea Smurra and Céline Zipfel discuss this question in “Young Adults and Labor Markets in Africa.” They write: “Today, one of every five people who start looking for their first job is born in Africa. By 2050, it will be one in three …”

The authors emphasize that while labor force participation rates are similar in African nations to other countries at similar levels of development, the real gap is in the number of young adults with employers and steady salaries. They point out that in a comparison group of developing countries, it’s more common for the elderly–who entered the labor force at an earlier time in the development process–to be self-employed and not salaried, but in Africa countries, younger and older adults are about equally likely to be self-employed and not salaried. Thus, existing development in Africa is not leading to more young adults having salaried jobs.

What might be done about this? The authors discuss possibilities for vocational skills training on the labor supply side, and for policies like wage subsidies or providing credit to help firms expand their hiring on the labor demand side. I was especially intrigued by what the authors call “matching policies”–basically, in the context of some African nations, it can be hard for employees to pay application fees, and hard for employers to have confidence in who they are hiring. What seem like relatively small interventions, like having the government pay the application cost for workers, or having a job search agency that provides advice on job applications and interviews, together with some verifiable tests on skills and personality, can make a substantial difference.

Is the political economy of Africa primed for the changes of development?

A standard concern about economic development in the nations of Africa is that the political institutions of these countries were unready to support the necessary policies. Nathan Canen and Leonard Wantchekon point out in “Political Distortions, State Capture, and Economic Development in Africa” that while this issue still applies, progress has been made. They write:

For example, sub-Saharan Africa saw a dramatic rise in democratic institutions of governance during the third wave of democratization in the 1990s, with Zambia, Cape Verde, and Benin as salient examples. This was spurred by the spread of democratic ideas, the end of the Cold War and the fall of the Soviet Union, the creation of robust local democratic communities, and the implementation of economic reforms(Huntington 1991). While only Botswana and Mauritius held regular multipartyelections by 1989, 33 of the region’s countries had held at least two sets of elections by late 2003 (Crawford and Lynch 2012). Figure 1 illustrates this change with data from the widely used Polity V database, produced by the Center for Systemic Peace, which collects components of governing institutions in 167 countries. These components are merged into an overall scale ranging from –10 (think “hereditary monarchy”) to +10 (consolidated democracy). Autocracies are scored from –5 to –10, and as Figure 1 shows, sub-Saharan Africa as a whole was in that category for much of the 1970s and 1980s. Since then, the Polity score for sub-Saharan Africa has risen substantially, approaching average world levels.

Economic outcomes in sub-Saharan Africa have also been converging to world norms. During the past 20 years, average GDP per capita in sub-Saharan Africa has more than doubled: from about $600 to close to $1600 (comparison using current US dollars, World Development Indicators data, as of June 2021). This wave of economic growth across sub-Saharan Africa is admittedly uneven. But while some countries still lag, economic growth rates in Rwanda, Ghana, and Ethiopia over the past 20 years resemble those in China and India, and the regional growth rates for Africa are comparable to those in regions like East Asia and Latin America.

The authors argue that some of the most important reforms for nations in Africa may be to help political institutions function in the broader public interest. They discuss campaign contribution limits, especially from corporations that are involved with government contracts; creating a nonpartisan civil service; having certain industry regulators elected rather than appointed; audits of how local governments spend money; using biometric identification to assure that government payments actually reach the intended person; public town hall meetings and debates; and others.

The nations of Africa have about 1.3 billion people, just a shade behind the populations of India and of China. Africa is also the region of the world where population is projected to grow most quickly in the next few decades. For an up-close look at an array of current research on development in countries of Africa, summaries of dozens of case studies from the annual conference of the Centre for the Study of African Economies is available here.

How Does the American Economic Association Invest?

The American Economic Association, the professional association for academic economists, holds about $50 million in investment assets. How do the professional economists invest?

To be clear, I’m not giving away any secret information here. The AEA is a nonprofit, and it needs to report publicly. A “Report of the Treasurer” is published each year in the AEA Papers and Proceedings: for the 2022 version, with spending in 2020 and 2021 and projections for 2022, see pp. 650–654). A Budget and Finance Committee manages the Association’s financial assets. Back in April 2017, it set the following “target portfolio allocations,” which have remained the same since then:

Total Stock Market Index Fund 35%
FTSE All-World Ex-US Fund 25%
Long-Term Investment Grade Fund 12%
Value Index Fund 10%
REIT Index Fund 5%
PIMCO PFORX Foreign Bond Fund 5%
Intermediate Bond Fund and local operating cash 8%

Perhaps this goes without saying, but this is not intended as personal investment advice. There is no reason for any individual investor to be following the AEA. Planning for your personal retirement, for example, raises quite different issue than the choices of an professional academic association. That noted, some of the broader principles guiding the choice of investment may be useful.

  1. The AEA does not deal with individual stocks, only with funds.
  2. The funds are passively invested, most of them through Vanguard, with very low costs of managing the money, not actively invested in a way that would involve higher costs–and would also involve choosing an active investment strategy.
  3. The AEA allocations have remained the same since 2017: clearly, there is no attempt here to jump in and out of various allocations.
  4. About 70% of the funds are invested in stock markets.
  5. The AEA makes some effort to diversify into international stock markets. The first two entries on the list operate through Vanguard, which is to say that they are low-cost passive funds, the first in the US market and the second focused on outside-the-US stock markets.
  6. The AEA also diversifies outside the stock market, into real estate and bonds.
  7. The one category where the AEA is relying to some extent on the irrationality of markets is the “Value Index Fund.” According to Vanguard, “This fund invests in stocks of large U.S. companies in market sectors that tend to grow at a slower pace than the broad market; these stocks may be temporarily undervalued by investors.” Another way to look at this fund is that it gives large US companies a slightly greater weight in the overall portfolio than they would otherwise have.  

You may also be wondering: Why does the American Economic Association even have $50 million? Starting back in 1969, the AEA got entrepreneurial. It started a project called EconLit, which creates an index of all articles in professional journals of economics. Back in 1969, this project was on paper and covered 182 journals. Now, it is a searchable online index covering over 1,000 economics journals, with abstracts describing many of the articles and with the ability to search the text of many of the underlying articles, too. This index is sold by subscription to research libraries around the world. In 2020, EconLit cost about $800,000 to produce, but brought in about $4.5 million in revenues–about 40% of total AEA revenues and by far the largest single amount. In turn, the AEA uses this financial cushion for other purposes: to keep membership dues low and to support nine different academic journals, as well as annual meetings, an index of job openings for academic economists, and other activities.

So one main reason the AEA has money to invest is EconLit. The other big reason is that the stock market has surged: for example, the S&P 500 stock index rose from about 800 back in 2009 to above 4,000 now, even after the declines since last December. In that situation, it would take a certain perverse genius not to make money in the stock market. But of course, what the stock market has given it can also take away, and that $50 million financial cushion as measured in September 2021 would already be substantially smaller today.

The New Opportunity Zones

The idea of an “opportunity zone” is to offer an incentive, whether through a grant or a a tax break, for people and firms to invest in a place with relatively low levels of income and jobs. Proposals along these lines have floated around for awhile under various names like “empowerment zones,” “enterprise communities,” “renewal communities,” and others. But the currently existing program of opportunity zones is a tax break that was discussed and proposed during President Obama’s administration, but became law as part of the Tax Cuts and Jobs Act signed into law by President Trump. The most recent issue of Cityscape, published by the US Department of Housing and Urban Development, has a symposium including research titles “An Evaluation of the Impact and Potential of Opportunity Zones” (2022, volume 24, number 1). I’ll provide the full Table of Content below. Here is an overview of some main insights.

In his introduction to the symposium, Daniel Marcin gives this quick overview of how the law works:

Opportunity Zones allow investors with capital gains to reinvest that money into Qualified Opportunity Funds (QOF), which then invest in OZs. Doing so has three main benefits.

1. The capital gains tax due on the original investment sale is deferred until the sale of the QOF investment or the end of 2026, whichever comes first.

2. If the investor holds the QOF investment for 5 years, the cost basis of the investment is
increased by 10 percent. If held for 7 years, or 2 additional years, the cost basis increases by an additional 5 percent.

3. If the QOF investment is held for 10 years, then no tax is due on any gains on the OZ
investment (IRS, 2021a).

As Marcin is quick to point out, this structure means that all evaluations of the program are preliminary. It took some time for the IRS to write up detailed rules of how they would work, and there has not been time for these holding periods of 5, 7, and 10 years to be completed.

An obvious question is what qualifies as an “opportunity zone.” The short answer is that state governors could make a list, within their state, of “low-income” areas defined as “generally a census tract with (a) a 20-percent poverty rate or higher, (b) a median family income of 80 percent or less than the metropolitan median family income, or (c) if not located in a metropolitan area, a median family income less than 80 percent of the state median family income.” For the record, a “census tract” is an area that usually contains from 1,200 to 8,000 people. There was also some wiggle room about choosing census tracts right next to these low-income areas. “Executives could select 25 percent of all tracts that were eligible, with a minimum of 25 in a state. In total, 8,766 OZs were designated.”

As you can imagine, there was also added complexity in how to decide if a business was really “in” an opportunity zone. For example, what if an existing business opened a small office in an opportunity zone? Not eligible. What if the business was physically based in the opportunity zone, but left the opportunity zone to provide services (like a housecleaning or landscaping company? Not eligible. In oversimplified terms: “The IRS ruled that, to qualify as an Opportunity Zone business, that business must earn at least 50 percent of its gross income from activity inside an OZ. … Opportunity Zone business property must be used “substantially all” of the time in an OZ.”

In their essay, Blake Christian and Hank Berkowitz argue: “The federal OZ program is arguably one of the most flexible, impactful, and bipartisan tax programs for helping disadvantaged communities in half a century.” They cite estimates that $75 billion had been invested in the opportunity zone program by the end of 2020. They point out that the program doesn’t just cover real estate, but also applies energy projects (like companies installing solar panels or insulation), infrastructure, active businesses, and public-private partnerships. Notice also that to get the tax breaks, the investor needs to commit to the investment for some years–in other words, this isn’t a tax break for flipping properties or other quick in-and-out investments. A common pattern seems to be that if the opportunity zone program can be combined with other business incentives, thus providing additional capital to (for example) a pre-existing program aimed at building more low-income housing.

It is fiendishly difficult to evaluate a program like opportunity zones. It’s unlikely that when governors were choosing among the census tracts eligible for opportunity zones that they did so at random. They may well have picked areas that they thought were more “ripe” for development. The funds going into opportunity zones could have been invested elsewhere–perhaps even in a neighboring census tract. In a broad sense, the gains from a program like this come from the sensible idea that investments to create jobs and economic activity in a depressed area have a bigger social benefit than similar investments in another area, because there are bigger spin-off benefits of improving economic activity in a depressed area.

That said, various studies in this issue give some promising results, for a program that has only existed for three years. One study found that OZ areas had seen faster growth of jobs and enterprises by about 2%. Another study found “that OZ tracts saw lower home price appreciation than did non-selected tracts before 2017. After 2017, however, OZ tracts had a 6.8-percent greater home price appreciation through 2020 over the eligible-but-not-selected tracts.” Another study found that if gentrification is defined as a “greater-than-average change in the percentage of tract residents older than age 25 with a bachelor’s degree,” then in Washington, DC, ” most OZs do not have a gentrification score higher than the city average.” In short, the gains from opportunity zones seem real, if modest.


An Evaluation of the Impact and Potential of Opportunity Zones

Guest Editor’s Introduction,” by Daniel Marcin

Enhancing Returns from Opportunity Zone Projects by Combining Federal, State, and Local Tax Incentives to Bolster Community Impact,” by Blake Christian and Hank Berkowitz

Missed Opportunity: The West Baltimore Opportunity Zones Story,” by Michael Snidal and Sandra Newman

The Failure of Opportunity Zones in Oregon: Lifeless Place-Based Economic Development Implementation Through a Policy Network,” by James Matonte, Robert Parker, and Benjamin Y. Clark

A Typology of Opportunity Zones Based on Potential Housing Investments and Community Outcomes,” by Janet Li, Richard Duckworth, and Erich Yost

Classifying Opportunity Zones—A Model-Based Clustering Approach,” by Jamaal Green and Wei Shi

The Impact of Qualified Opportunity Zones on Existing Single-Family House Prices,” by Yanling G. Mayer and Edward F. Pierzak

Gentrification and Opportunity Zones: A Study of 100 Most Populous Cities with D.C. as a Case Study,” by Haydar Kurban, Charlotte Otabor, Bethel Cole-Smith, and Gauri Shankar Gautam

Collaboration to Support Further Redevelopment and Revitalization in Communities with Opportunity Zones,” by Michelle Madeley, Alexis Rourk Reyes, and Rachel Bernstein

Tax Cuts, Jobs, and Distributed Energy: Leveraging Opportunity Zones for Equitable Community Solar in the D.C. Region,” by Sara Harvey

Census Tract Boundaries and Place-Based Development Programs,” by Joseph Fraker

Competition Via Variety

One of the standard concerns about measuring economic output is that it doesn’t take variety into account, just the total amounts bought and sold. If variety increases, consumers are likely to feel better off from the wider array of choices, but if they same amount is bought and sold, GDP does not care about variety. This seems problematic, especially because the number of choices available to shoppers seems to be rising, and the large packaged good companies are using additional variety–rather than, say, lower prices or higher quality of existing goods–as a primary method of competing.

Áine Doris reviews and discusses this trend in “Do Shoppers Have Too Many Choices? US consumer goods are proliferating rapidly, with implications for consumers and companies” (Chicago Booth Review, May 23, 2022). Who among us has not stood for a few minutes, gobsmacked in the grocery, trying to sort out which size and variety of snack chip should go into the cart? As Doris writes: “Bags of Tostitos Scoops! tortilla chips share shelf space with bags of Tostitos Scoops! Multigrain or Tostitos Hint of Lime, while cans of Diet Coke vie with those of Coca-Cola California Raspberry and Coca-Cola Cherry Vanilla Zero Sugar. The seemingly endless options stretch beyond the food and drink aisles to shelves offering diapers, detergents, stationery, soaps, coffee, cosmetics, and more.”

A study looking at 118 products groups in consumer packaged goods found that the “number of `niche’ alternative products increased by 4.5 percent a year from 2004 to 2016,” which works out to a total increase of 70% in that time.

However, this growth in variety has been accompanied by consolidation in the number of companies in the consumer packaged goods category. Another study found: “[A]lmost half of US product markets—including food and beverages, health care, apparel, and electronics, as well as a slew of nonmanufacturing markets, such as insurance and financial services—were `highly concentrated’ or dominated by two or three multinationals. Over time, the likes of General Mills, Nestlé, Procter & Gamble, and Unilever have systematically acquired and subsumed other consumer brands.” 

At least so far, this combination of more dominant firms selling a greater variety of products seems to have worked out fine for consumers. During this time, markups for these products remained about the same, so consumers were in effect getting more variety without boosting the profit margins of firms. Doris writes: ” Although there are fewer companies offering products in a certain sector (say, food products), there are more companies offering them in a specific market (such as chips). That is creating more competition at the level of individual products, which keeps prices low.”

In part, what seems to be happening is that small companies producing these kinds of consumer packaged goods do the innovation, and then if a small company has a successful product, it is bought by one of the big companies. The big companies have economies of scale in production, distribution, and marketing, so they can sell the new product at a lower cost than the smaller firm.

Access to the web, targeted advertising and home delivery is another major change. A small-scale producer doesn’t have to scratch and claw for shelf space in a major grocery or retail chain. Instead, it’s possible to use targeted web advertising and promotions, which are quite inexpensive relative to the historical patterns of large-scale advertising on television or in print, to bring in a group of customers who can then have the product delivered directly to their home. Doris cites some examples:

The upshot is that it got easier for newcomers to disrupt markets. The internet eradicated the need for a huge marketing budget or even a physical store. Warby Parker started advertising and selling affordable glasses online, then upturned the eyeglass industry. In apparel, Bonobos set about redefining the retail experience in men’s apparel by blending physical and online platforms; consumers can secure a good fit in a store but do their actual shopping online. (Walmart took notice and bought the company in 2018.) Dollar Shave Club, known for its viral video advertisements on social media, destroyed Gillette’s stranglehold by creating an alternative to expensive razors and selling them online, giving users the option to purchase upgrades or add-ons. Unilever purchased it in 2016., founded in 2005, was scooped up by Amazon five years later.

From a broad social point of view, it’s not clear how to think about these patterns. On one hand, the growth of big firms that could exploit market power to raise profits is a traditional source of concern. The idea that these big firms could entrench themselves by buying the smaller firms that might grow up and have a chance of challenging their dominance someday is concerning, which has been a real concern with the big tech companies like Google and Facebook. On the other side, the current system does give entrepreneurs some powerful incentives, because if they can just get their company established they can cash out by selling it to a bigger firm. At least so far, consumers are getting more variety and (at least in the data that is available) the big consumer packaged good firms have not been charging higher markups.

As Doris points out, there is also a possible conflict brewing here. The new consumer product goods are often designed around values like being locally produced, or being healthier to consume. If this kind of product is bought up by a big company, but keeps its brand name, will consumers still feel the same way about it a few years later? Does the big company even know about how to promote a locally-produced, health-based product?

A final issue is called the “paradox of choice,” which is the idea that consumers who are overloaded by choices may be less happy with their ultimate choice, because of a fear that they missed out on something better, or may even decide to shy away from buying at all. There is limited evidence of this happening in some contexts. For example, one study looked at restaurant delivery platform that kept adding more options. Up to some point, the additional options brought in more business; beyond that point, additional options seemed to discourage possible consumers and brought in less business. But from a broad perspective, the appetite for additional variety in consumer packaged goods seems to be very strong, and so the pattern of large firms using variety to compete within narrow categories of products seems likely to continue.