On COVID-19 and Classes

I am sharing a letter that I just sent to my department listserv. If you share my sentiments, please feel free to pass this message to your listserv:

Dear colleagues,

I want to share my personal views and plans for how I am going to respond to the COVID-19 concerns. After today’s class, I am moving all of my classes online.  I want to encourage you to do so too.

COVID-19 is clearly contagious and lethal.  Today, I heard WHO estimates of a 3% fatality rate. That’s greater than the Spanish Flu.  Now, the rate may prove to be far lower once we factor in undetected survivals and the (purportedly) better quality of US healthcare, but the fact remains that this virus is far more deadly than regular flu, and regular flu is itself surprisingly lethal.

Now, many people are quick to point out that otherwise healthy younger and middle aged people seem to fare well in the face of this virus.  First, young people can die of this.  Moreover, this is a communicable disease, and every young person who gets infected can bring that home to someone older or infirm.  These are people too.  Encouraging young people to congregate is to unwittingly ask someone to further risk a vulnerable loved one.

Moreover, Yale’s Nick Christakis points out on Twitter that, even if we cannot ultimately prevent the eventual spread of COVID-19, reducing travel, interpersonal contact, etc. will reduce the pace at which the healthcare system processes COVID-19 cases.  This might ultimately lessen the ultimate impact of the disease.  Institutions like schools can play a role on preventing travel, contact, and transmission.

Many public communications about COVID-19 responses cite what seem like low numbers of cases in New York.  I have no confidence in these numbers.  Unlike places like South Korea or Canada, there is no widespread testing for COVID-19.   Doctors cannot get tests, and there are numerous reports of people with attention-worthy symptoms and travel histories being denied testing.  There has been some sort of failure in the public health system.  For all we know, the virus could have spread far further.  It has certainly spread into the college-aged population, with Yeshiva University closing today. I’m moving forward on an assumption that there’s an outbteak developing here.

To my mind, the proactive decision is to move online now, instead of waiting until you are forced by CUNY.   From what I see, there’s a widespread paralysis.  We don’t know what is going on, and we don’t know what to do.  I actually sympathize with everyone further up the food chain from me.  The higher up you are, the bigger the ramifications of telling everyone to abandon campus.  In some respects, it is probably best for the academics to use their collective power to make the best choice, which is going online.

I want to pass along a wonderful take from our brilliant young colleague Charlie Gomez: Maybe we can find an opportunity in this crisis.  There are so many reasons to get serious about online – both as a personal job skill and as an organizational initiative.  Maybe it is time to get serious about modernizing.

If any of you want to talk about this, I’m happy to have a Skype lunch meeting later this week.


Announcing the Measuring Socialism Data Set

A new, freely-available data set measuring countries’ conformity to free market/socialist ideals.

Tired of the old, impressionistic, non-rigorous Venezuela vs. Norway arguments about socialism? Would you like to make systematic comparisons based on empirical data?

Joseph van der Naald and I are happy to share Measuring Socialism, a data compilation of 240+ variables that measure of 43 countries’ semblance to free market/socialist ideals. You can use these data to develop empirical assessments of countries’ economic systems, and then test propositions linking socialism or free markets to political, economic or social outcomes.

The data is free to download (data and codebook). The scripts used to generate the set are available as well through the Open Science Framework.

The Distribution of Total Education Debt among U.S. Households

How big are U.S. households’ education loans?

The topic of education debt and its impact on U.S. households has attracted more interest with Senator Elizabeth Warren’s recent proposals for a publicly-subsidized debt relief plan. This memo is the first in a series examining the impact of education debt on household balance sheets and cash flows, using data from the Survey of Consumer Finances1.

In this memo, I examine the distribution of household education debts. I examine the prevalence of education debt, and the overall size of education debts held by households.


Prevalence of Education Debts

Many U.S. households carry education debt, although most do not. The data suggest that about 22% of US households owe any money on educational loans. Typically, these debts helped cover the household heads’ education. Slightly more than 19% owe money on their own loans, and just under 4% owe money for their childrens’ education. In other words, just over one-fifth of American households stand to benefit from an education debt reduction scheme.

Distribution of Education Debts

The median educational debt-bearing household owes $19,000. One quarter owe less than $8,310, and another quarter owe more than $41,600. The top ten percent of debtors carried more than $80,000 in student debt The figure below depicts our estimates of total education debts among debt-bearing households:

Distribution of education debt loans among education debtor households, United States, 2016. Source: Federal Reserve

Note that the x-axis has a custom scale. Had we used evenly-spaced categories, the figure would have a strong right skew. Six-figure education debts are quite rare among educational debtors, and are very rare in the population at large. A proposal to eliminate up to, say, $50,000 in student debt would benefit a large majority of houseohlds. Keep in mind that these are houseohld figures, which would consolidate the debts of two married people would enjoy even more relief if debt relief is provided on a per person basis, as is the case with Senator Warren’s most recent proposal.2

When interpreting these figures, remember that this is the prevalence of debt among the 22% of households with education debts. So, for example, the 10% of education debtor households who owe more than $80,000 corresponds to 2.2% of the overall country. Maintaining a cognizance of this point is important, because it means that people with more than, say $40 thousand, is in fact a rarity in society-at-large.

First Impressions

I’m sure more proposals for educational debt relief will emerge over the course of the Democratic primary. There might be quibbles over who would qualify for it. Proposals will differ over whether to offer relief in the thousands or tens of thousands, and how much maximum relief to offer. There are limits to which conclusions we can draw.

Many Will Be Affected

The data do make it clear that education debt relief is a program with a decently-wide franchise, in the sense that it affects about as many households as a major entitlement program. Presumably, the program would cost less than a major entitlement, because debt relief is generally a one-shot event, as opposed to a continuing expense. If this debt relief program comes without structural changes to education pricing or lending, then there are some prospects for the student debt problem to just start up again, which might cause subsequent generations to ask for this precedent to be repeated. Without concurrent structural adjustments to the industry, then one might ask whether debt forgiveness is just an economic payout to a targeted constituency.

Most Debtors Seem to have Manageable Debts

It seems like most people have relatively modest education debts, around the size of the cost of a new car. I would assume that these kinds of debts could easily be managed by an employed, college-educated household over a five- or ten-year term. Of course, not everyone who takes out debts graduates with a degree, and not all degree-holders maintain employment. These are all questions that can be probed further with this data, if there is interest.

First Reactions After Seeing Data

My first exposuree to these data have led me to pause in supporting higher education debt forgiveness. My initial reflex is to want these kinds of programs, because I believe that unaffordable higher education is a problem. However, I am not convinced that relieving past debts, especially debts this large, would address my reasons for wanting more government investment in education access. I have a lingering suspicion that proposals like Warren’s would benefit a lot of wealthy people who purchased very expensive schooling. I am less concerned with this “problem” – I see this behavior as an already-rich person’s attempt to purchase their way up the ladder. I’m more concerned with the kind of students we serve at CUNY, for whom a $7,000 tuition is a heavy lift because they lack the parental support of their more privileged counterparts.

In a world of limitless resources, I’d say great — let’s do it all. However, political and in turn fiscal realities mean that certain efforts at progressive economic reform will have to hit the chopping block. My first reaction is to think that this is good candidate for early cuts.

  1. Federal Reserve Bank (2017) Survey of Consumer Finances, 2016. Online database.
  2. It is possible to make more precise estimates from the data, but I will bracket the development of precise evidence here. I can follow up if anyone else is interested.

For More

You can download the R Markdown file used to generate these results from Open Science Framework. The data used in this analysis is available for download here.

Sociocast Wins Award

I am very proud to announce that I have been awarded the 2019 Public Sociology award from the American Sociological Association’s Section on Communications, Information Technology and Media Sociology section. This award was conferred for my work on The Sociocast Project Thank you very much for this award. I am very honored.

I have several people to thank. First and foremost, I am deeply indebted to my two co-hosts and old, dear friends: Leslie Hinkson and Gabriel Rossman. You are both so smart and so good at this. I love that we’ve found a way to get together each week. It’s one of the best parts of this project.

Second, I would like to thank my colleagues in Queens College’s Department of Sociology. Throughout my time here, you’ve always let me do sociology off the beaten path. You are all so supportive, and I am grateful to work somewhere that really gives its people the trust and latitude to do different things. A very special thank you to Dana Weinberg and Andrew Beveridge.

Third, I am very grateful to have worked with such terrific Senior Producers, Anika Chowdhury and Lisseth Moreno. You have both been invaluable to this project. The project would not have been able to accomplish so much without you. I have no doubt you will grow to be outstanding media professionals, and that I will be bragging to have known you before you were big shots.

Finally, thank you to the sociology community for their support. This discipline is brimming with talent. Honestly, all you have to do is get people in a (virtual) room, hit record, and harness the brilliance.

Thank you to everyone who has supported this podcast. I’m thrilled to get this award.

I Bought My Book’s Digital Distribution Rights

I used my royalties to buy my book’s digital distribution rights, and am now selling it for $2.99 on Amazon Kindle.

The biggest mistake that I made in negotiating my first solo book contract was not thinking about selling price. When you are an Assistant Professor, you are so focused on getting tenure that you don’t think about book prices. You need peer acceptance (read: letters), and it is possible to get a free copy to the main players in that process. I just wasn’t focused on my book’s list price.

After I got tenure, my book’s price became a sore spot for me. The book was listed at $60 a copy. I’m sure that, in a strict economic sense, that price renders the maximum total book revenue. It hits price insensitive parts of the book market, like libraries and mandated student purchases, and milks each individual buyer as if they were six people buying $9.99 Kindle editions. A book that’s mostly dry-ish household finance data analysis is only going to sell so many copies, so the revenue maximizer tries to get as much out of each potential customer.

The problem is that profit maximization and audience reach are two different things. Who was going to pay that much for an academic book on household finance? Not me. I could not bring myself to encourage people to buy a book that expensive, and there was no chance that I’d ever require my students to pay that kind of money. You can encourage people to ask their librarians to order the book, but you are going to lose tons of readers going through libraries. So many intentions to read die on the inter-library loan vine.

So I used some of my book royalties to buy digital distribution rights from my publisher. I am selling an Kindle edition of my book for the minimum allowable price: $2.99, and will be distributing free copies through my web site in the fall. Along with free electronic copies of my book, I will distribute single chapter PDFs, high quality media for use in classroom slides, and chapter summaries.

The book is a study of financial insecurity in the U.S., and its relationship with the high cost of wellbeing-essential products due to America’s neglected public services. Here an interview about the book:

The book’s chapters can be used as a basis for a range of interesting class discussions, like:

  • Are people really worse off today?
  • Exactly how have people’s money situations gotten worse over the past few decades?
  • Who counts as “rich”, and how much money do they really have?
  • Are poor people really so poor?
  • How many of us are ultimately reliant on government aid?
  • Why can’t people just tighten their belts in response to financial problems?

And many more. Check it out!

Long-Term Changes in Government Spending

Governments are big compared to the 19th century, but their size has generally been stable over the past several decades, except perhaps in the US.

Many economic policy arguments portray the public sector as having grown to be very large – perhaps even excessively so. This factual claim sets the groundwork for policy reforms designed to shrink the public sector. How big are public sectors today? Are they unprecedentedly large, as austerity advocates often claim? Or have governments been downsized in the era of neoliberalism? In this analysis, we examine changes in government expenditures since the 19th century.


This analysis uses government expenditures (% GDP) data from Mauro et al.’s (2013)1 Public Finances in Modern History database2 This metric gives a rough estimate of how much the government spends relative to economic production. A higher number suggests that the government is larger relative to the economy under its jurisdiction.


Today’s U.S. government are large by historical standards. Public sectors are several times larger than they were before the Great Depression and World War II. Since the 1950s, the public sector appears to have been growing steadily. Growth was considerable during the 1960s through mid-1980s, and experience a slowdown during the 1990s and 2000s. After 2007, US public expenditures rose rapidly. Figure 1 shows changes in the ratio of government expenditures to GDP since 1800 for the United States:

Note that, even during the Civil War and WWI, government expenditures were a fraction of their present-day levels. Spending in 2013 was nearing peak spending levels during World War II. Since 2013, government expenditure to GDP levels have fallen to around 38% (not depicted in these data).

US is not Unique. The U.S. is not unique. It’s experience is typical of highly developed countries. Consider Figure 2 (below), which shows similar plots of several other countries for which long-term data was available. All countries experienced considerable growth in government after World War II, but the pace of that growth had been arrested by the 1980s:

Note that, while the US figure gives a stronger indication of a post-1980 growth trajectory, its overall levels are lower relative to most of these other countries.


Governments grew considerably after the Great Depression and World War II. This growth seemed particularly intense in the 1960s and 1970s, but eventually this growth was halted in the 1980s. The data suggest that government spending has been reasonably stable in most countries since 1980, although spending did rise temporarily in the US after their economic crisis. So, compared to 19th century America, the US has a huge and expansive government. By modern standards, its government is comparatively small, and government spending has been relatively stable across rich countries since then.

  1. Mauro, Paolo, Rafael Romeu, Ariel Binder, and Asad Zaman (2013) “A Modern History of Fiscal Prudcence and Profligacy” IMF Working Paper WP/13/5
  2. Distributed by the International Monetary Fun as “Public Finances in Modern History Database, 1800 – 2011”, data and documentation available for download at and on this project’s online depository

For More

You can download the R Markdown file used to generate these results from Open Science Framework.

Entrepreneurship and Race, 1989 – 2016

A look at differences in entrepreneurship participation rates across race.

This analysis examines the prevalence of entrepreneurship across U.S. households by race from 1989 to 2016. We are looking for racial differentials in entrepreneurship participation, and evidence of changes over time. The data suggest that entrepreneurship rates have been relatively constant over the time period studied, and that whites are more likely to participate in entrepreneurship.


We use data from the Survey of Consumer Finances1 We use the convention of identifying entrepreneurs through a “self-employed” labor market status or through reported possession of an actively managed busienss.2 For race, we focus on the race of the households’ head, as defined in the data. Of course, there are many issues to unpackage when contemplating the Survey’s traditional methods for designating household heads, but we will bracket them here.


Overall, the data appears to suggest that little has changed in self-employment rates across major race/ethnicity groups.


Our first figure shows estimates of entrepreneurship participation rates from 1998 to 2016. These figures represent the estimated percent of U.S. households with at least one household head who is either self-employed or owns an actively-managed business.

Our estimates suggest that white-headed households’ entrepreneurship rates have been stable since the late-1990s. One can read the figure as suggesting that rates rose slightly among households whose heads chose to self-identify as black or Hispanic. If such an observation is indeed picking up on a definitive change, and is not a produce of random variation masquerading as a small trend, then one might not that most of this growth seems to have occurred in the early-2000s, and rates show no clear evidence of substantial growth afterwards. However, one could just as easily infer that there’s been very little change in entrepreneurship participation rates. That interpretation strikes me as the most conservative.


Our second figure focused on self-employment rates only. The figure suggests that these rates were mostly stable among whites.

As with overall entrepreneurship participation rates, self-employment is highest among white-headed households, and has been relatively stable over the past several decades. One could read the data on Hispanic-headed households to suggest a slow long-term growth between 2001 and 2004, and stable rates thereafter. Among black-headed households, self-employment rates are lowest, and have been overall stable until 2016. It seems most prudent to wait for evidence from other data or subsequent years before believing that something changed in black self-employment rates.

Business Ownership

Our final figure looks at business ownership rates. Again, entrepreneurship is most common among white-headed households, and rates have been overall stable across major racial/ethnic groups.


Overall, this look at the data suggest that entrepreneurship participation rates have been stable over time across major racial/ethnic groups. Despite a range of policies and investments in the promotion of entrepreneurship, these rates have not risen considerably.

  1. Federal Reserve “Survey of Consumer Finances” Data from triennial survey, 1989 to 2016. Available for download at
  2. SCF variables x4106, and x4706. We only consider self-employment among household heads.

For More

You can download the R Markdown file used to generate these results from Open Science Framework. The data used in this analysis is available for download here.

Road to Serdom? Data Suggests No

The bivariate relationship between government expenditures (% GDP) and World Governance Indicators, 2015

A common argument against socialism is that it paves a path to corruption and authoritarianism. In essence, it is a political argument based on an assertion that an enlarged government decreases society’s governance quality, or puts society at greater risk of a collapse in governance quality. Governance quality is a technical term in the development literature whose meaning closely resembles the more popular political concept of “good government”. It refers to the degree to which a government is stable, rule-bound, competent, publicly accountable, and acts in accordance with the public interest.

With all due deference to the scenarios dreamed up by Friedrich Hayek, is there anything to this argument? You should not base your beliefs on whether it is endorsed by someone who is famous or respected, but rather whether or not you see evidence that the argument is true. This analysis looks at bivariate relationships between government expenditure levels and countries scores on the World Bank’s World Governance Index.

Our findings suggest countries with larger governments tend to be better governed, perhaps in part because better developed economies tend to have bigger governents.


To probe the relationship between big government and bad governance, I draw on World Bank data. Size of government is measured by government expenditures (% GDP).1 The measurement strategy is based on the notion that a government that provides more services, performs more functions, or assumes an expanded purview will require more supplies, labor, and other resources. To measure governance quality, we consdier four metrics from the World Governance Indicators:2 Voice and Accountability, Political Stability and the Absence of Violence, Rule of Law, and Concrol of Corruption. These data are based on meta-analyses of survey data caputring experts’, businesspeople’s, diplomats’ and others’ opinions on countries governance quality. All of these variables are measured on standardized scales, with mean = 0 and SD = 1. We look at data from 2015.


We review the results of four comparisons. A point to note: Readers might not that Switzerland, Canada, and the United States register government expenditure levels that appear to be low. These figures likely exclude these governments’ sizable subnational government spending. These measurement issues are likely to understate the strength of our results: that countries with bigger governments tend to be better governed as well. Note that all these figures exclude micro-states (i.e., with populations of less than one million).

Voice & Accountability

Our first figure presents the relationship between govenrment expenditures and the WGI’s Voice and Accountability Index.This index captures “perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.”

The figure immediately gives us a sense of this relationship. Given that better-developed or wealthier countries tend to have larger governments3, it makes sense that countries with larger governments are also perceived to have more accountable, more democratic governments. This is a fairly strong correlation of 0.51

Political Stability & the Absense of Violence

Political Stability & the Absence of Violence, which measures “perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism.” This figure visualizes a correlation of 0.21

Rule of Law

Rule of Law, which captures “the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.” Corrlation: 0.39.

Control of Corruption

Control of Corruption, which measures “perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as”capture” of the state by elites and private interests.” Correlation: 0.36


These findings do not strongly establish the view that bigger governments improve governance, but they certainly cast doubt on the proposition that bigger governments lead to worse governance. Better developed, wealthier countries tend to have bigger and better functioning governments.

Anyone who wishes to make the case that big government leads to worse governance needs to explain how their view can be maintained despite the fact that a straightforward comparison suggests that the opposite is true.

  1. Variable GC.XPN.TOTL.GD.ZS in World in World Bank (2019) World Development Indicators Online database.
  2. World Bank (2019) World Governance Indicators Online database
  3. see Cohen, Joseph N. 2019. “Government Revenues to GDP.” Retrieved (

For More

You can download the R Markdown file used to generate these results from Open Science Framework. The data used in this analysis is available for download here.

How Big is America’s Tax Burden?

A look at the distribution of government revenues to GDP.

One often hears arguments that America’s government is too large. People evoke the imagery of a bloated, gluttonous, over-reaching, and domineering public sector as a justification for policy reforms that cut taxes on businesses, privatize public assets or enterprises, deregulate markets, and cut social programs. In this note, I probe the proposition that the United States’ public sector is large through an examination of government tax statistics.

Assessing Public Sector Size through Expenditures

The analysis uses tax revenues (% GDP) to estimate the size of the U.S. public sector. Tax revenues capture “the amount of money that governments spent on good and services, including supplies and labor.”compulsory transfers to the central government for public purposes.” We scale this figure to countries’ GDP, to get a sense of federal government taxes levels relative to overall economic activity (as proxied by value-added output). So a country with a high score has a government that collects a lot of money relative to the size of the economy under its jurisdiction. We can infer that its central government is large compared to a country with a public sector that spends at comparatively low levels.

I use data from the World Bank’s World Development Indicators.1 We use 2015 data, because it renders a good balance between recency and low missingness.  Figure 1 (below) depicts the distribution of tax hauls:

Revenue to GDP

Distribution of Tax Revenue to GDP across Countries, 2015

Wagner’s Law

The size of the U.S. public sector looks less low-but-typical when we compare it against other wealthy countries. Consider the second figure below, which presents a scatterplot of government expenditures (% GDP) to per capita GDP levels (all data from the World Bank). A blue line depicting the results of a bivariate regression are superimposed. We distinguish OECD members in blue.

Taxes and Per Capita GDP
Scatterplot of Tax Revenues (% GDP) to Per Capita GDP, 2015

There are 103 countries represented in this figure, with the bins set at intervals of 2.5 percentage points. In this data, the United States registers a tax to GDP ratio of 11.2%. This is below our sample mean of 16%, but within one standard deviation (SD = 6.6%). The figure suggests that a typical government size, as measured by taxes (% GDP), is 10 to 22 percent. In other words, governments typically spend the equivalent of one-tenth to one-quarter of their national economic output.

The relationship is positive, suggesting that more developed countries have larger public sectors. The figure is illustrating a well-known concept in public finance called Wagner’s Law, although the law itself refers to spending rather than revenue. Taxes and spending roughly track each other. The Law, named after turn-of-the-20th century economist Adolph Wagner, posited that the increasing sophistication of modern economies require that a government assume more functions or jobs to keep markets running and sustain people’s economic wellbeing. As our economy becomes larger and more complex, then society comes to depend on more government services to keep itself running. There is no need for a pharma, nuclear power, or derivatives markets regulators if there is no pharma, nuclear, or derivatives industry. There was less need for Social Security when we died younger. We didn’t need public investments in higher education when virtually everyone farmed for a living.

Consolidated Government Expenditures

One problem with the World Development Indicators’ data is that it does not include the tax burden levied by all levels of government. When making global comparisons, this tends to render reasonably fair comparisons, because sub-national government taxes are generally not substantial. However, these burdens are substantial in the United States (as well as Canada and Switzerland), where the central government has devolved many of its operations. We are able to make these comparisons with some degree of confidence among the wealthy countries using data from the OECD’s Government Revenue Statistics2. We are looking at total tax revenues (% GDP) for 2015.

Again, U.S. tax levels are near the bottom of the OECD, and only surpass levels of newer, lower-income members who only recently gained accession to the organization.

Americans’ Tax Burden

From this vantage point, the U.S. government seems to spend seems well below what one would expect, given its high levels of wealth. It’s government a pretty big for a poor country, but quite small for a wealthy country. This lends itself to an interesting interpretation of society’s choice about socialism versus capitalism. Were America to dramatically increase the size of its government, it would still register as quite typical among wealthy countries. However, were it to really reduce the size of its government, it would be a more extreme outlier. The position to cut government is the radical ones, given that the vast majority of developed countries have had much larger public sectors.

  1. Metric GC.TAX.TOTL.GD.ZS from World Bank (2019) World Development Indicators Online database.

For More

You can download the R Markdown file used to generate these results from Open Science Framework. The data used in this analysis is available for download here and here.

Zero Sales Businesses

A substantial proportion of entrepreneurs aren’t actually selling anything.

This analysis is interested in the prevalence of paper entrepreneurs, people who self-identify as self-employed or operating their own business but whose business registers no sales. An example of this kind of entrepreneur might be an unemployed person who works as a “consultant” (and lists themselves on LinkedIn as such), but do not have any clients. Another might be someone who claims to have an actively-managed business in order to construe and write off household expenses as business expenses in order to maximize tax deductions.

I expected to see a secular rise in the prevalence of no sales businesses for at least three reasons. First, I was under the impression that America’s tax system and business enviornment evolved to encourage small businesses formation. Many “zero sales” businesses might be startups at their very earliest stages of development. Given overall rates of entrepreneurship seem to have remained unchanged1, I expected the pool of small businesses to be have proportionally more startups than in the past.

Second, I presumed that the evolution of taxes would create incentives for households to declare business ownership and push household transactions through those businesses as a tax management strategy. I thought that either new regulations or laws created new opportunities to save money in this way, or that new technologies made it easier for people to recognize and capitalize on business deductions (e.g., Turbotax may alert people to the potential for business deductions, and give people a means to capitalize on them).

Third, the title of “entrepreneur” is more socially desirable than that of a retiree or unemployed, and I expected more people to assume these roles when creating public identities on web sites like LinkedIn or on their resumes. Given that job precarity and the forced “retirement” of older workers are widely-discussed as major trends, I thought there was a potential for this dynamic to raise the proportion of “zero sales” entrepreneurs.

Data and Methods

We use data from the Survey of Consumer Finances2 This analysis only considers entrepreneurs who own actively-managed businesses. The other group traditionally counted among entreprneurs – those who self-identify as self-employed – are not included because their business transacting data is not in the data set. We are interested in the percentage of households whose actively-managed businesses collectivelygenerate no sales. If a household has one business with sales, then they are not countes as zero sales entrepreneurs.


The figure below depicts the estimated percentage of business owners who make no sales. Since 1992, the proportion of actively-managed businesses that registered no sales appeared to bounce within the range of 8.8% (in 2001) and 11.8% (1995). I am not strongly confident that the large change from 1989 to 1992 represents a real change, or whether it is a byproduct of any differences in the survey or sampling mechanisms in these surveys.


To some degree, this was an unanticipated finding to me. I found many reasons to expect a rise in these kinds of businesses. These failed expectations could be the result of faulty presumptions, including:

  • Zero sales businesses may not signal business startups
  • Tax policies or economic regulations did not change the ease of forming new businesses
  • Tax policies or technology did not change in a way that sufficiently encouraged enough people to form businesses for the purpose of tax management.
  • Comparatively few people usurp the role of “entrepreneur” to cover up unemployability

For More

You can download the R Markdown file used to generate these results from Open Science Framework. The data used in this analysis is available for download here.

  1. Cohen, J. N. (2019, March 1). Prevalence of Entpreneurship among U.S. Households, 1989 – 2016. Retrieved from <>
  2. Federal Reserve “Survey of Consumer Finances” Data from triennial survey, 1989 to 2016. Available for download at