Edward Conard

Top Ten New York Times Bestselling Author

  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Friday, February 12, 2021

AI Summary. About 1 in 10 prime-age American men is neither working nor in education or training; these men out of the labor force spend roughly 7.5 hours a day on leisure, including nearly 1,900 hours a year on screens, over 300 hours more than unemployed men seeking work. Nearly half report daily pain medication use.

Nicholas Eberstadt American Enterprise Institute
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Prior to pandemic, 1 in 10 prime-age American men was neither employed nor in education or training. They spend 7.5 hours daily on leisure, totaling 2,700 hours annually, with nearly 1,900 hours spent in front of screens.

How do prime-age men out of the labor force spend their days?

Core argument: Prime-age American men not in the labor force spend about 7.5 hours a day on leisure, roughly 2,700 hours a year, more than employed men.

Prior to the pandemic, 1 in 10 prime-age American men was neither employed nor in education or training (NEET). These men, categorized as NILF [Not in Labor Force], spend about 7.5 hours daily on leisure activities, totaling approximately 2,700 hours annually—significantly more than their employed counterparts. A substantial portion of this time, nearly 1,900 hours a year, is spent in front of screens, akin to a full-time job. This screen time is over 300 hours more than that of unemployed men actively seeking work. Additionally, nearly half of NILF men reported daily use of pain medication, suggesting a lifestyle marked by disengagement and potential substance dependency. This data highlights a critical socio-economic issue, as these men remain detached from the workforce and skill development opportunities.

Takeaways by Macro Roundup® AI

  1. Prime-age American men not in the labor force spend about 7.5 hours a day on leisure, roughly 2,700 hours a year, more than employed men.
  2. Men not in the labor force spend nearly 1,900 hours a year in front of screens, over 300 hours more than unemployed men actively seeking work.
  3. Nearly half of prime-age men not in the labor force reported daily use of pain medication, and before the pandemic 1 in 10 prime-age American men was neither employed nor in education or training.

Prior to pandemic, “…in contemporary America, the overwhelming majority of prime-age male NILFs are NEETs: in the years 2015-19, according to Census Bureau data, fewer than one in six NILFs was an adult student. In the lead-up to the COVID pandemic, this meant one in 10 prime-age men was neither working, nor looking for work, nor seeking the skills that might help them return to the workforce….”

How prime age men not in labor force and also not in school or job training spend their days, "...If we disaggregate prime-age NILFs into NEETs and adult students, two strikingly different ways of life are revealed. On the one hand, adult students reportedly spend an average of nearly six hours a day on their education or training—and since those averages include weekends and holidays, these men are committing over 2,100 hours a year to their schooling. The converse of such motivation is an unusually low involvement in “socializing, relaxing, and leisure”—distinctly less than for working men, though not as little as for prime-age working women, a notoriously “leisure-poor” population. On the other hand, self-identified prime-age NEET men spend about seven and a half hours a day in “leisure activities.”2 That works out to about 2,700 hours a year—almost 1,600 hours a year more than working women, nearly 1,400 hours a year more than working men, and remarkably enough, over 450 hours a year more than unemployed men…”
This leisure time is largely spent on digital devices (in terms of screens) and for a good many of them on drugs"...NEET men reported an average of over five hours a day in front of screens—nearly 1,900 hours a year, almost equivalent to the time commitment of a full-time job. ATUS does not ask specifically about video games; if it did, even more NEET screen time commitment would almost certainly be recorded. To go by the time-use surveys, prime-age men without work who are not looking for jobs and not engaged in training spend almost three times as many hours in front of screens as working women and well over twice as many as working men.3Strikingly, they also report over 300 hours more screen time per year than their unemployed counterparts—men likewise jobless but who want to get back to work. And the reality is even more disturbing than these time-use numbers can convey on their own. According to a 2017 study by Alan Krueger, almost half of NILF men reported taking some form of pain medication every day. The fraction for NEET men would likely be higher still. The rhythms of life for a great many of the prime-age men in America currently disengaged with the world of work is defined not simply by days and nights sitting in front of screens—but sitting in front of screens while numbed or stoned...."

What do prime-age NILF men do all day?: Extended Excerpt Image 1


Nicholas Eberstadt, "What do prime-age ‘NILF’ men do all day?," American Enterprise Institute, February 8, 2021, https://www.aei.org/articles/what-do-prime-age-nilf-men-do-all-day/

Thursday, February 18, 2021

AI Summary. The expansion following the Great Recession was long and slow, with labor market slack initially restraining wage growth; real wages at the median began rising only after several years, and those at the 20th percentile later still. Productivity growth was slower than in previous cycles, limiting gains in living standards.

Michael Strain National Bureau of Economic Research
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Had 2009 expansion ended in 2014, real wages at median and 20th percentile would have been under 2007 level @MichaelStrain @nberpubs.

Does a long expansion take years to deliver wage gains?

Core argument: Had the expansion that began in 2009 ended in 2014, real wages at the median and the 20th percentile would have been below their 2007 levels, as labor market slack held back wage growth.

The recovery from the Great Recession was marked by a slow and steady expansion, with significant labor market slack initially holding back wage growth, particularly for lower-income workers. Had the 2009 expansion ended in 2014, real wages at the median and 20th percentile would have been below 2007 levels. It took over 9 years for unemployment to fall to the CBO's natural rate, with the economy adding jobs for 113 consecutive months starting in October 2010. Real wage growth began to accumulate late in the expansion, with median wages rising from 2012 and bottom quintile wages from 2015. The length of the expansion was crucial in tightening labor markets, eventually leading to wage gains across the income distribution. Despite these gains, productivity growth remained notably slower compared to previous cycles, impacting overall living standards and wage growth.

Takeaways by Macro Roundup® AI

  1. Had the expansion that began in 2009 ended in 2014, real wages at the median and the 20th percentile would have been below their 2007 levels, as labor market slack held back wage growth.
  2. Unemployment took over 9 years to fall to the CBO's estimated natural rate, and the economy added jobs for 113 consecutive months starting in October 2010.
  3. Real median wages began rising in 2012 and bottom-quintile wages in 2015, while productivity growth remained slower than in previous cycles, affecting living standards and wage growth.

new Strain that might be a useful citation at some point documents the slow recovery from the Great Recession. Slow and steady (eventually) won the race - their story is large labor slack held back wage growth, especially at the bottom of the distribution. Eventually, workers got real gains. Deflating with PCE, workers at bottom quintile saw real wage gains on average of 1% a year but all of that came late in the expansion. They find no evidence of overheating going into pandemic as inflation was check.

Good chart of real hourly wage growth by percentile seems to show a real pivot in 2014. Had the 2009 expansion ended in 2014, real wages at the median and 20th percentile would have fallen below their 2007 levels.

The Recovery From The Great Recession: A Long, Evolving Expansion: Extended Excerpt Image 1


In terms of wages and participation, “…In the previous 3 recessions, it took 5-6 years for the unemployment rate to fall to the CBO’s estimate of the natural rate of unemployment. Following the Great Recession, it took over 9 years for the economy to reach that point, as shown in Figure 3…The shock to labor markets was in many ways worse than the loss of output. The loss of employment (8.6 million jobs, or 6.3 percent of prior peak) was massive, roughly twice as bad as prior recessions. The 1982 recession had a maximum job decline of 2.8 million (3.1 percent of peak); the 1991 recession had a drop of 1.5 million jobs (1.4 percent of peak) and the 2001 recession had a loss of 2.6 million jobs (2.0 percent of peak.)Figure 1 shows the depth of the shock in sharp relief. This employment drop from peak to trough (6.3 percent) was notably larger than the output drop (4.0 percent).In addition, unemployment rates spiked, doubling and briefly rising to 10 percent. The more rapid drop for employment than output generated some odd statistical quirks. Labor productivity growth was strong at first as output fell less than hours worked. The compositional shift in which workers were still employed meant that wages actually rose - with average hourly earnings of production and non-supervisory workers continuing to grow at roughly 4 percent a year (the same as their pace in the prior two years) despite the massive shock and extensive slack in labor markets…. A slow and steady recovery followed the Great Recession’s official end in the summer of 2009, but because it was slow and the depth of the recession so deep, it took years to reduce slack in labor markets. The share of the population that was employed stayed well below prerecession levels despite steady job growth for years, and wages grew slowly. But because the slow-and-steady recovery lasted so long, many pre-recession peaks were exceeded, and eventually real wage growth began to accumulate for workers across the distribution. In fact, the business cycle (including recession and recovery) beginning in December 2007 was one of the better periods of real wage growth in many decades, with the bulk of that coming in the last years of the recovery….The economic expansion following the Great Recession included the longest streak of job growth on record. Beginning in October 2010, the economy added jobs for 113 consecutive months. The streak ended in March 2020 when the COVID-19 recession began…The length of the expansion allowed it to go through different phases. Early in the expansion, jobless rates remained stubbornly high, participation rates continued falling, and wage growth was slow. Compared to their outcomes prior to the recession, the least-skilled, lowest-wage workers in the labor market fared considerably worse than higher-skilled, higherwage workers. But by 2014, wages began to grow faster at the median. By 2015, prime-age labor force participation began to increase. In the last five years of the expansion, wage growth had picked up at the bottom of the income distribution, outpacing gains in the middle and at the top of the distribution.Employment rates for the workers with the least education rose further above their pre-recession level than those for college graduates. Vulnerable workers saw their labor market prospects improve considerably. The length of the expansion — which allowed the labor market to tighten — was critical to both bringing people back into the workforce and seeing wage gains pushed across the income distribution. Later in the expansion, though, real wages began to grow for all workers. Median wages began rising in 2012, and wages at the bottom of the distribution began rising in 2015. For the last five years of the expansion, in fact, real wages were rising most rapidly for workers at the bottom quintile….”

The Recovery From The Great Recession: A Long, Evolving Expansion: Extended Excerpt Image 2


Jay Shambaugh and Michael Strain, "The Recovery From The Great Recession: A Long, Evolving Expansion," National Bureau Of Economic Research, February 2021, https://www.nber.org/papers/w28452

In terms of productivity, “…Productivity, on the other hand, was notably slower in this recovery. Growth in output per hour worked (measured peak to peak) was well below that of the prior three business cycles. Scholars of productivity often note different eras of productivity growth booms and the last one from 1995-2004 generated good productivity gains in the 90’s and 00’s expansions, but during the 10’s expansion,there was never a return to such a level or even to the average productivity growth of near 2 percent of the prior 50 years.Lower productivity growth slows living standard growth and slows wage growth. The causes of the reduced productivity growth are too complex and numerous to delve into here, but may include a shifting industry mix towards services, a reduction in business dynamism, increased concentration in some sectors, insufficient R&D spending (including at the federal level), problems in the intellectual property regime, insufficient infrastructure and other capital spending, regulatory shifts, and simple limits of growth. In addition, though, it is distinctly possible that the extensive slack in labor markets reduced the need for productivity enhancements during much of the expansion…”

AI Summary. Global warming is projected to cause an average welfare loss of about 6%, with losses reaching 15% in Central and South America, South Asia, and Africa due to lower productivity and worse amenities, while Siberia, Canada, and Alaska may gain through migration. The authors identify migration and innovation as key adaptation mechanisms.

Jose Luis Cruz National Bureau of Economic Research
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Average global welfare loss from climate change is ~6%, with the largest losers concentrated in Central/South America, South Asia, and Africa.

How unevenly does global warming affect welfare across regions?

Core argument: Global warming is projected to cause an average welfare loss of about 6%, with losses reaching 15% in Central and South America, South Asia, and Africa, driven mainly by reduced productivity and adverse effects on amenities.

Global warming is projected to cause an average welfare loss of ~6%, with significant regional disparities. Central/South America, South Asia, and Africa face welfare losses as high as 15%, primarily due to reduced productivity and adverse effects on amenities. In contrast, regions like Siberia, Canada, and Alaska may experience gains driven by migration. The study highlights the role of migration and innovation as key adaptation mechanisms. Carbon taxes, while delaying fossil fuel consumption and flattening temperature curves, are more effective when paired with abatement technologies. The choice of discount factor significantly influences the optimal carbon tax, underscoring the complexity of policy decisions aimed at balancing current and future welfare impacts.

Takeaways by Macro Roundup® AI

  1. Global warming is projected to cause an average welfare loss of about 6%, with losses reaching 15% in Central and South America, South Asia, and Africa, driven mainly by reduced productivity and adverse effects on amenities.
  2. Siberia, Canada, and Alaska may gain welfare from global warming, with those gains driven by migration, which the study identifies alongside innovation as a key adaptation mechanism.
  3. Carbon taxes delay fossil fuel consumption and flatten temperature curves, and are more effective when paired with abatement technologies, while the choice of discount factor significantly influences the optimal carbon tax.

Jose Luis Cruz and Esteban Rossi-Hansberg, “The Economic Geography of Global Warming,” National Bureau Of Economic Research, February 2021, https://www.nber.org/papers/w28466

Just an FYI, Rossi-Hansberg on the economic topography of the likely impact of climate change. Basically tries get a net impact (in economic terms) Siberia, Canada and Alaska for example gain, whereas you have major negative impacts in Central America, South Asia and Africa. They use a discount rate of 9%(…In our baseline scenario we use a discount factor of β = 0.965 in an economy where real GDP grows around 3% per year….”)

Lots of the northern gains are driven by an inflow of migration, the losses (~ half) are driven by negative effects on productivity

Bottomline, “…. Our baseline results show welfare losses as large as 15% in parts of Africa and Latin America but also high heterogeneity across locations, with northern regions in Siberia, Canada, and Alaska experiencing gains.Our results indicate large uncertainty about average welfare effects and point to migration and, to a lesser extent, innovation as important adaptation mechanisms. We use the model to assess the impact of carbon taxes, abatement technologies, and clean energy subsidies. Carbon taxes delay consumption of fossil fuels and help flatten the temperature curve but are much more effective when an abatement technology is forthcoming… Overall, Figure 14 shows that the effect of temperature on fundamental productivity and the effect of temperature on amenities, each account for about half of the total welfare losses from global warming…”

“….Table 2 presents the global average real GDP and welfare losses from global warming across different tax levels and discount factors, with respect to a scenario in which environmental policies are absent. Our choice of discount factor is limited by the balanced-growth-path growth rate. In order to obtain finite present discounted values of welfare and real GDP for all future paths, we chose a baseline discount factor of β = 0.965. 47For this value, carbon taxes are not desirable today. The largest present discounted value of real GDP or welfare in Table 2 is obtained for τ = 0. However, if we increase the discount factor to β = 0.969, a carbon tax of 200% or more maximizes welfare and real GDP. This large sensitivity of the optimal carbon tax is natural given the path shown in Figure 19 and cautions us not to rely too heavily on PDV statistics that depend on specific values of the discount factor. Ultimately, the discount factor used determines the policy-maker’s preferences for the welfare of current versus future generations…”

Ed Comment:“Presumably the discount rate is 0.9% not 9% which would be highly unusual for a global warming study. The graph seem to show the future in the future (not discounted to the present). So it’s not clear from the summary what they are discounting. I don’t take much from this other than the negative consequences of warming seem further off than I expected. Again, solving a problem 100 years from now using today’s technology seem highly uncertain to produce any result. 100 years ago, had we tried to solve global warming caused by today’s prosperity, using horses what could we have possible done? Wouldn’t the optimal answer (by far) have been wait and solve it later? They say we are 6% below what we would have been, but how much more prosperous are we then vs now? A lot more I presume. Do they estimate the expected effect on America — both the growth and the lose? x 94% is 4 times richer 100 years from now than today at 1.5% per year growth rate. What growth rate do they assume for the US and the world?”

The Economic Geography of Global Warming: Comments Image 1

Note their time horizon is so far off I’m not sure it’s that helpful, “….With the quantified model in hand, we can simulate the economy forward over several centuries and evaluate the economic consequences of global warming. This phenomenon is expected to have heterogeneous effects over space, where the hottest regions in South America, Africa, India and Australia experience welfare losses of 15% and the coldest regions in Alaska, Northern Canada, and Siberia undergo welfare gains as high as 14%. On average, the world is expected to lose 6% in terms of welfare, although the exact number depends on the yearly discount factor.3 By 2200, the average loss in welfare is 10% and in output larger than 4%, although the uncertainty inherited from our estimated damage functions implies that the 95% confidence intervals include loses as high as 20% and 12%, respectively. The large uncertainty in average outcomes, however, does not translate into significant uncertainty on the spatial distribution of losses. The relative distribution of loses is very similar in our baseline case compared to the worst or the best-case scenarios (as measured by the 95% confidence intervals of our damage functions). When we decompose the losses coming from the effect of global warming on amenities or productivity we find that about half of the average effects come from the impact of temperature rise on productivity. Effects on amenities are particularly important for loses in Africa and gains at the most northern latitudes; while losses in productivity affect almost all regions to the south of the 30◦ latitude….”

Steve Comment: “Sorry fat fingered that, yes their discount rate is.9%, not 9%. They have a global growth rate (BGP or Balanced Growth Path) converging on slightly above ~3% a year. They don’t give estimates for the United States itself. “….To evaluate the welfare consequences of global warming, we compute the present discounted value (PDV) of local utility that is not idiosyncratic, namely, P∞ t=0 β tut(r). We also present results for the discounted value of real income, P∞ t=0 β tyt(r). We use a value of the discount factor of β = 0.965. 35Our choice of the discount factor is restricted by a real output growth rate that is slightly larger than 3% per year.Clearly, to do proper comparisons we need a discount factor for which present discounted values remain finite in all exercises…””

Steve Comment: “Sorry fat fingered that, yes their discount rate is.9%, not 9%. They have a global growth rate (BGP or Balanced Growth Path) converging on slightly above ~3% a year. They don’t give estimates for the United States itself. “….To evaluate the welfare consequences of global warming, we compute the present discounted value (PDV) of local utility that is not idiosyncratic, namely, P∞ t=0 β tut(r). We also present results for the discounted value of real income, P∞ t=0 β tyt(r). We use a value of the discount factor of β = 0.965. 35Our choice of the discount factor is restricted by a real output growth rate that is slightly larger than 3% per year.Clearly, to do proper comparisons we need a discount factor for which present discounted values remain finite in all exercises…””

AI Summary. Trump's 2020 loss turned on a combined 42,918 votes across Arizona, Wisconsin, and Georgia, a narrower margin than the 77,736 votes across Michigan, Pennsylvania, and Wisconsin that delivered his 2016 win. The author argues this shift of decisive states from the upper Midwest to the Sunbelt means Republicans can still win by adapting to that changed map.

Michael Barone New York Post
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@MichaelBarone Trump’s defeat in 2020 was by a narrower margin: 42,918 in AZ, WI, GA; 65,009 including NE-2 than his win in 2016, 77,736 in three tipping point states.

Can Republicans win by adapting to a shift toward Sunbelt swing states?

Core argument: Trump's 2020 defeat turned on 42,918 votes across Arizona, Wisconsin, and Georgia, a narrower margin than the 77,736 votes in Michigan, Pennsylvania, and Wisconsin that delivered his 2016 victory.

In the 2020 election, Trump's defeat was by a narrower margin of 42,918 votes in AZ, WI, and GA compared to his 2016 victory margin of 77,736 votes in MI, PA, and WI. Including NE-2, Biden's margin was 65,009, still less than Trump's 2016 win. This shift from the upper Midwest to the Sunbelt states indicates changing political dynamics that could influence future elections. The GOP's ability to perform well without Trump hinges on adapting to these evolving electoral landscapes. The data suggests that while Trump's unique approach impacted his 2020 loss, the GOP's broader electoral strategy can still succeed by focusing on these key states.

Takeaways by Macro Roundup® AI

  1. Trump's 2020 defeat turned on 42,918 votes across Arizona, Wisconsin, and Georgia, a narrower margin than the 77,736 votes in Michigan, Pennsylvania, and Wisconsin that delivered his 2016 victory.
  2. Including Nebraska's 2nd district, Biden's margin was 65,009 votes, still smaller than Trump's 2016 winning margin.
  3. The decisive states shifted from the upper Midwest in 2016 to the Sunbelt in 2020, and the GOP's ability to win without Trump hinges on adapting to those states.

Barone factoid, ".....Trump’s idiosyncratic approach to COVID-19 and his refusal to propitiate hostile constituencies produceddefeat by an even narrower margin (42,918 votes in three states) than his victory in 2016 (77,736 votes in three states)...."

Michael Barone, "GOP will do fine — with or without Donald Trump,"New York Post, February 14, 2021, https://nypost.com/2021/02/14/gop-will-do-fine-with-or-without-donald-trump/

(note ME-2 is Trump)

Here is the map giving Trump Arizona, Wisconsin and Georgia but giving Biden the Nebraska seat (Which Obama also won in 2008)

My work:

Steve Comment, Note the tipping point states in 2016 were all in the upper Midwest, Michigan, Pennsylvania and Wisconsin. Whereas in 2020 they had shifted to sunbelt, Arizona, Georgia and Wisconsin. I had crunched these numbers before. this stat is ~ right (and directionally is correct) the person who did it initially forgot Nebraska, I will make a note to the entry. Not sure how the WSJ fact checker missed it. In 2016 Trump won Michigan, Pennsylvania and Wisconsin by77,744(combined votes) (I’m relying on Dave Wasserman for that number) Check In 2020 Biden won those three states by300,857combined votes for context However if you go by the closest margins that put Biden over the finish line last year (Arizona, Wisconsin, Georgia and Nebraska 2 CD) his margin of victory was65,009, less then Trump’s win in 2016. If you don’t count Nebraska it was 43,000, but that mattered! See map.

AI Summary. Marriage status is associated with differences in US voting and ideology: 45% of married Americans voted for Trump in the 2020 election, versus 20% of unmarried people living with a partner and 18% of single Americans. None of the groups is ideologically uniform — 32% of married Americans identify as conservative, 23% as liberal, and 45% as moderate.

Samuel Abrams American Enterprise Institute
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Married Americans voted 45% for Trump, 20% of unmarried partners and 18% of single Americans did the same.

Does marital status correspond with differences in voting and ideology?

Core argument: In the 2020 election, 45% of married Americans voted for Trump, versus 20% of unmarried Americans living with a partner and 18% of single Americans.

In the 2020 election, 45% of married Americans voted for Trump, compared to 20% of unmarried individuals living with a partner and 18% of single Americans. This data highlights a significant correlation between marriage status and voting behavior, suggesting that marriage is a higher priority for those with conservative views. However, married Americans are not a monolithic conservative group; 32% identify as conservative, 23% as liberal, and 45% as moderate or leaners. Single Americans lean left, with 16% conservative, 32% liberal, and 52% moderate. Unmarried partners resemble singles, with 38% liberal, 10% conservative, and 52% moderate. These statistics indicate that while marriage status influences political preferences, none of these groups is ideologically homogeneous.

Takeaways by Macro Roundup® AI

  1. In the 2020 election, 45% of married Americans voted for Trump, versus 20% of unmarried Americans living with a partner and 18% of single Americans.
  2. Married Americans are not ideologically uniform: 32% identify as conservative, 23% as liberal, and 45% as moderate or leaners.
  3. Single Americans lean left, with 32% liberal against 16% conservative and 52% moderate, while unmarried partners are similar at 38% liberal, 10% conservative, and 52% moderate.

Samuel Abrams reports that marriage status was linked to voting behavior in 2020 (note this doesn’t appear to control for age) “…A support rate not matched by other groups,45 percent of married Americans voted for Donald Trump. For those who are unmarried and living with a partner, only 20 percent voted for Trump, and just 18 percent of single Americans voted for Trump.Without question, there was a strong relationship between marriage and voting in 2020….”
To Abrams this suggests marriage is a higher priority for more conservative individuals, “….In short, marriage is generally a higher priority for people with a more conservative worldview. Married Americans were appreciably more likely to vote for Donald Trump in 2020 compared to single Americans, but married Americans are not a single bloc of conservatives…. About a third (32 percent) of married Americans identify as conservative, while almost a quarter (23 percent) identify as liberal, with the plurality (45 percent) moderate or leaners.This is a slight lean to the right, but hardly a lopsided distribution of Americans. Single Americans lean to the left, but this is also not extreme. Only 16 percent of singles are conservative and 32 percent are liberal, but the majority (52 percent) are in the middle.Those who are unmarried but living with a partner look similar to singles, with close to two-fifths on the left (38 percent) and a tenth identifying as conservative (10 percent). But the bulk are again in the middle (52 percent). None of these groups is ideologically homogeneous, or dominated by one side or the other….”

Wilcox’s work is attached.

Samuel Abrams, "Married Americans are different," American Enterprise Institute, February 16, 2021, https://www.aei.org/op-eds/married-americans-are-different/

AI Summary. Married voters favored the Republican presidential candidate, with 56% of married men and 52% of married women backing Trump, while unmarried voters favored Biden, at 52% of unmarried men and 62% of unmarried women.

Peyton Roth American Conservative
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@PeytonRoth 2020 voting breakdown by marital status and gender: 56% and 52% of married men and women voted for Trump, respectively; non-married men and women voted for Biden by 52% and 62%, respectively.

Do married and unmarried voters back different political parties?

Core argument: In the 2020 election, 56% of married men and 52% of married women voted for Trump, a pattern of married voters favoring the Republican candidate.

In the 2020 election, 56% of married men and 52% of married women voted for Trump, highlighting a significant trend among married voters favoring Republican candidates. Conversely, non-married men and women showed a preference for Biden, with 52% and 62% respectively supporting the Democratic candidate. This voting pattern underscores a demographic divide that may influence future economic policies and political strategies, as married individuals often prioritize issues like tax policies, family benefits, and economic stability. Understanding these preferences is crucial for policymakers and economists aiming to address the needs of diverse voter groups and anticipate shifts in electoral outcomes that could impact macroeconomic conditions.

Takeaways by Macro Roundup® AI

  1. In the 2020 election, 56% of married men and 52% of married women voted for Trump, a pattern of married voters favoring the Republican candidate.
  2. Non-married voters broke for Biden in 2020, with 52% of non-married men and 62% of non-married women supporting the Democratic candidate.

Peyton Roth and Bradford Wilcox, "Married Americans Keep Voting Red,"American Conservative, February 9, 2021, https://www.theamericanconservative.com/articles/married-americans-keep-voting-red/