Edward Conard

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  • “…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
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  • “…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
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  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
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Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Tuesday, January 7, 2020

AI Summary. The trade-off between unemployment and inflation known as the Phillips Curve shows significant negative slopes and nonlinearities in metropolitan-area data even though national data since the 1980s show weakened evidence of it. The authors attribute the divergence to monetary policy being external to local areas and greater statistical power from larger samples.

Amir Sufi National Bureau of Economic Research
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The Phillips Curve appears to function at the MSA level but not at the national level, with MSA data showing significant negative slopes & nonlinearities, suggesting it’s still relevant locally.

Does local data show the unemployment-inflation trade-off still holds?

Core argument: Metropolitan Statistical Area–level data show significant negative Phillips Curve slopes and nonlinearities between unemployment and inflation, while national data since the 1980s show weakened evidence of that relationship.

The Phillips Curve, which traditionally shows a negative relationship between unemployment and inflation, appears to function at the MSA [Metropolitan Statistical Area] level but not at the national level. Since the 1980s, national data has shown weakened evidence of this relationship due to the endogeneity of monetary policy and limited variation in the unemployment gap. However, MSA-level data reveals significant negative slopes and nonlinearities, suggesting that the Phillips Curve is still relevant locally. This discrepancy may be due to the exogeneity of monetary policy at the MSA level and more statistical power from a larger sample size. The national data's inability to capture steep slopes at low unemployment rates further explains the divergence. Thus, while national inflation dynamics seem subdued, local data indicates that a high-pressure economy could still lead to inflationary pressures.

Takeaways by Macro Roundup® AI

  1. Metropolitan Statistical Area–level data show significant negative Phillips Curve slopes and nonlinearities between unemployment and inflation, while national data since the 1980s show weakened evidence of that relationship.
  2. The national–local divergence is attributed to the endogeneity of monetary policy and limited variation in the unemployment gap nationally, versus exogenous monetary policy and greater statistical power from a larger sample at the MSA level.
  3. National data fail to capture the steep Phillips Curve slopes at low unemployment rates found in MSA data, suggesting that a high-pressure economy could still generate inflationary pressures.

Prospects For Inflation In A High Pressure Economy: Is The Philips Curve Dead Or Is It Just Hibernating?: Extended Excerpt Image 1

Peter Hooper, Frederic S. Mishkin, and Amir Sufi, "Prospects For Inflation In A High Pressure Economy: Is The Philips Curve Dead Or Is It Just Hibernating?" National Bureau of Economic Research, May 2019, https://www.nber.org/papers/w25792

"....National data going back to the 1950s and 60s yield strong evidence of negative slopes and significant nonlinearity in those slopes, with slopes much steeper in tight labor markets than in easy labor markets. This evidence of both slope and nonlinearity weakens dramatically based on macro data since the 1980s for the price Phillips curve, but not the wage Phillips curve. However, the endogeneity of monetary policy and the lack of variation of the unemployment gap, which has few episodes of being substantially below zero in tis sample period, makes the price Phillips curve estimates from this period less reliable. At the same time, state level and MSA level data since the 1980s yield significant evidence of both negative slope and nonlinearity in the Phillips curve. The difference between national and city/state results in recent decades can be explained by the success that monetary policy has had in quelling inflation and anchoring inflation expectations since the 1980s.We also review the experience of the 1960s, the last time inflation expectations became unanchored, and observe both parallels and differences with today. Our analysis suggests that reports of the death of the Phillips curve may be greatly exaggerated....Figure 3.2 shows a scatterplot and regression line of nominal wage inflation against the unemployment rate for the state-year level panel. The sample covers the 50 states and the District of Columbia from 1981 to 2017. To ensure that the scatterplot matches the regression specifications below, both the unemployment rate and nominal wage inflation are first regressed on state and year fixed effects before being plotted. As a result, the scatterplot contains deviations from the state and year mean for each state-year level observation. As the table below shows, the estimated coefficient on the unemployment rate is -0.41. Figure 3.2 shows a steep slope in the state-year level wage-Phillips curve. A state with a negative deviation from its normal unemployment rate sees a larger than average increase in nominal wage inflation. Column 1 of Table 3.1 shows the point estimate of the regression line, which is -0.41. Column 2 of Table 3.1 includes a control variable for lagged core price inflation at the regional level...The inclusion of this control variable is meant to capture inflation expectations or any other factor that may be determined by lagged inflation in that region. The point estimate on the unemployment rate is almost identical. The specification reported in Column 2 includes year fixed effects, which implies that the negative slope coefficient is not driven by national inflation dynamics. The state-year wage-Phillips curve shows a significant and steep negative slope.. the coefficient estimates for the wage-Phillips curve are similar for the national- and state-level specifications, both in terms of the level and the evidence for nonlinearities.... The state and MSA data, which has many more observations with very tight labor markets, suggests that the Phillips curve with nonlinearities might be alive and well. Thus the reports of the death of the Phillips curve might be greatly exaggerated. We should not discount the possibility that substantial nonlinearities are present in today’s Phillips curve and so a high-pressure economy could lead to inflationary pressures. Second, our evidence, both in the macro, time-series data and the state and MSA data in Sections 4 and 5, suggests that the wage-Phillips curve is alive and well.This is consistent with the observation that wage inflation has marched steadily, if slowly, upward in recent years as the labor market has tightened, as shown in Figure 6.1. However, Chair Powell (2018b) has noted, we did see something like this in the late 1990s-early 2000s without an inflation lift (Figure 6.2). But that was also a period during with strong dollar helped keep a tight lid on inflation, and the tight labor market was cut short by the recession of 2001, Could we see this play out again? Wage inflation did rise significantly in the late 90s (both in nominal terms and in excess of productivity trends), as it had in the mid 1960s. While the wage Phillips curve may have flattened some over time, it remains much steeper and evidence of nonlinearities much more robust than for the price Phillips curve in the post-1988 sample period. It is possible that the link between wage and price inflation has become weak, but this is a puzzle that requires further research..."

Overall findings:

Additionally they speculate that, “…. another possible reason for the steeper slope of the Phillips curve using MSA-level data is that this slope is not affected by the endogeneity of monetary policy. Monetary policy is the same for all MSAs and so is necessarily exogenous in this data. Hence the MSA-level data would not be subject to the bias that flattens the estimated slope of the Phillips curve and so could display steeper Phillips curve estimates. While the regional level data may help better identify the slope of the Phillips curve given more variation in the employment environment and the exogeneity of monetary policy, this comes at a theoretical cost. Such state and MSA level specifications are less well grounded in theory….”

They also note that the steeper estimates for MSA (Metropolitan Statistical Areas) are likely driven by sample size,"…One potential reason for the steeper estimate in MSA-level data is more statistical power for identification...If the price-Phillips curve is non-linear and especially steep at unemployment rates below 4% (as suggested by Table 3.2), the national level specification after 1988 will have a difficult time identifying this effect. In fact, from 1988 to 2017, there is only one year (2000) in which the unemployment rate reached 4% or lower. The post 1987 national sample Phillips curve estimate is produced with almost no data from the unemployment regime in which the MSA-level data say the slope is steepest. In contrast, there are a large number of observations (115) in the MSA-year level data with unemployment rates below 4%...."

Reviewed paper Sufi paper mention in WSJ that found Phillips curve working on local level. You asked, “ ….how do they reconcile local price increases drive by tight utilization to tight national utilization without increases?...”

Overall they finds that the difference btw national and city/state results can be explained by several factors (though they don’t attempt to estimate each factors contribution)“…. The difference between national and city/state results in recent decades can be explained by the success that monetary policy has had in quelling inflation and anchoring inflation expectations since the 1980s…”

Ed Commented:“Plz read and summarize the study below. How do they reconcile local price increases drive by tight utilization to tight national utilization without increases? You would think it would be the opposite: if utilization were low in some regions (rural) then the rest (urban) would be tight (if people weren't moving there) and wages would be rising despite low national utilization. If inflation were reflected in high real estate prices but wages were not rising as much then after-rent wages would be falling. That would seem to make Borges argument if immigrants were moving to faster growing cities with higher real estate costs and accepting lower after rent wages. Perhaps real estate prices are low in immigrant neighborhoods because higher-wage whites won't move there. That would hide the true cost of real estate to whites”

AI Summary. The authors estimate that workers without college degrees moved from a housing-inclusive urban wage premium of over 10% in 1970 to an urban penalty of over 10% by 2015, driven by rising housing costs and falling relative wages. College-educated workers continued to earn a substantial urban wage premium over the same period.

Daniel Shoag American Enterprise Institute
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Non-college workers have seen a decline in urban wage premium, turning into a housing-inclusive urban wage penalty of nearly 20% btw 2000 & 2015. @DanielShoag

Have housing costs turned the urban wage premium into a penalty?

Core argument: Non-college workers moving from low- to high-density U.S. areas earned a housing-inclusive wage premium above 10% in 1970, but faced an urban penalty of over 10% by 2015.

Over the past 50 years, non-college workers have experienced a significant decline in the urban wage premium, turning into a housing-inclusive urban wage penalty of nearly 20% between 2000 and 2015. In 1970, these workers could expect a housing-inclusive wage premium of over 10% when moving from low to high-density areas, but by 2015, this shift resulted in an urban penalty of over 10%. This trend is driven by escalating housing costs and declining relative wages, making high-density areas less attractive for non-college workers. Conversely, college-educated workers continue to enjoy a substantial urban wage premium, with hourly wages increasing by approximately 50% over the same period. The disparity highlights the economic challenges faced by non-college workers in urban settings, as housing costs negate potential wage benefits.

Takeaways by Macro Roundup® AI

  1. Non-college workers moving from low- to high-density U.S. areas earned a housing-inclusive wage premium above 10% in 1970, but faced an urban penalty of over 10% by 2015.
  2. Between 2000 and 2015, the housing-inclusive urban wage penalty for non-college workers reached nearly 20%, attributed to rising housing costs and declining relative wages.
  3. College-educated workers retained a substantial urban wage premium over the same 50 years, with hourly wages rising roughly 50% when moving to high-density areas.

Philip Hoxie, Daniel Shoag and Stan Veuger, "Moving to density: Half a century of housing costs and wage premia from Queens to King Salmon," American Enterprise Institute, December 2019, https://www.aei.org/wp-content/uploads/2020/01/Hoxie-Shoag-Veuger-Moving-to-Density-WP.pdf

"....Have workers stopped moving to the highest-density, highest-productivity places in the country because of a decline in the urban wage premium, or because the rent is too high? We analyze how important these two explanations are by studying them in one and the same empirical analysis. We find that that non-college workers now effectively face a housing-inclusive urban wage penalty, while workers with college education continue to face a significant urban wage premium.....Our results suggest that the decline in the urban wage premium for non-college workers has been especially steep once housing costs are taken into account. This has had dramatic consequences for the attractiveness of high-density areas:for non-college workers, there is now, on average, an urban wage penalty after accounting for the cost of shelter....find that non-college workers now effectively face a housing-inclusive urban wage penalty, while college workers continue to face a significant urban wage premium...For college workers, shown in blue, the steep gradient of the slope relating wages to prior density shows that the wage premium has remained large over the past half century. A back-of-the-envelope calculation may be helpful in interpreting the size of the wage premium here: whether one moves from 2.7 to 3.1 on the log scale, as in the 1970 panel, or from 2.9 to 3.3, as in the 2015 panel, hourly wages increase by some 50%. On the other hand, workers with only a high school degree or less, shown in red, have seen their wage premium in dense areas decline, especially after the year 2000, from about 35% in 1970to 10% in recent years. This decrease in high-density wages for low-skilled workers makes migration toward high-density areas less attractive for such workers, ceteris paribus...Incorporating housing costs does not change the steep urban wage premium facing workers with at least some college much. However, non-college workers saw that housing-inclusive premium starting to erode during the 1980s, before watching it turn into an urban wage penalty of almost 20% between 2000 and 2015. This means that not only have relative wages decreased dramatically for non-college workers in dense areas, their housing costs have escalated as well...Panel B shows that, in 1970, non-college workers could expect a housing inclusive wage premium of over 10 percent when moving from the 25th percentile of commuting-zone density to the 99th percentile of commuting-zone density. In 2015, that same move would instead be associated with an urban penalty of over 10 percent. The wage gradient is not only negative in 2015, but at all densities non-college workers are worse off in real terms in 2015 than at any other point on the table...."

New from your friend Stan Veuger (and Shoag of Ganong/Shoag) looks at the urban wage premium, supports Ganong/Shoag and finds that non-college workers are now net negative if you include housing costs.

Ed Comment:“I took a first read of your paper over the weekend (a topic that interests me greatly) and love where you are heading. Congrats. Two aspects of the data have always given me pause—differential real estate appreciation by relevant neighborhood/demographic (i.e., high-skilled vs low-skilled) and migration of non-native-borns. Obviously, an influx of high-skilled workers raises real estate prices on average, especially real estate desirable to high-skilled workers. Increased demand for real estate from an influx of high-skilled workers surely spills over into low-skilled neighborhoods at the margin. Although given the share of high-skilled workers (say 5%) to low-skilled workers (66%), one might suspect the spillover is smaller than the sky-is-falling media admits. We care more about the dynamics of low-skilled housing price appreciation relative to low-skilled wages, and why an increase in low-skilled real estate prices lowers low-skilled after-rent wages (rather than proportionately raising low-skilled wages), especially when real estate restricts the supply of low-skilled labor. We know from Moretti that low-skilled wages are significantly higher in faster growing cities (i.e., compensated for some of the higher costs). You would also think that as low-skilled wages and real estate prices rise, low-skilled workers would find it increasing economical to commute longer distances to capture higher wages and avoid higher priced real estate. Given these dynamics, I can’t help but wonder if an influx of non-native-born low-skilled workers willing to accept lower after-rent wages has squeezed low-skilled after-rent wages rather than an influx of high-skilled workers. It’s not hard to imagine low-skilled migration patterns being further exacerbated by ethnic non-native born immigrants finding ethic low-skilled neighborhoods (and schools) more desirable than native-borns, and vice versa.”

Ed Comment: “Housing expenditures as a share of income segregated by income level solves my concern about segregating the housing data.I’m still left looking for reasons/evidence why low-skilled urban after-rent wages have fallen, especially if the supply of real estate isn’t restricted by something other than replacement cost (i.e. unrestricted other than the cost of commuting). I suppose one reason could be changing preferences where people might spend a greater share of their income on housing (and healthcare) as other things grow cheaper (or they price the growing benefits of urbanization into real estate). But that doesn’t explain why native-borns are/seem to be affected differently (i.e. migrating away instead of to urban areas).One answer is native-borns owned houses in urban areas and are now selling them to consume their imbedded equity (probably as part of a retirement strategy) rather than suffering lower after-rent wages. But I still have a sneaking suspicion that without the non-native born low-skilled migration, we are missing an important part of the picture. What other than a growing demand for urban jobs and low-skilled urban real estate (in ethnic neighborhoods) would drive down low-skilled urban after-rent wages significantly?Whenever I try to hire domestic employees, they insist on working off the books unless I pay them their taxes. I suppose if they can work next door for more, I would have to pay the difference. But if I have to pay their taxes, why wouldn’t I have to equilibrate all the additional costs necessary for them to urbanize? It’s hard to imagine urban labor markets as monopsonies. Again, it leads you to believe the demand for urbanization must growing despite the increased cost—a contradictory conclusion if native-borns are moving away.”

Stan Comment:“Thanks for the note, Ed. We’re planning on looking at the migration issues a little more closely. Keep in mind though that there are still quite a few places where housing costs don’t exceed construction costs by much. I’m less concerned about residential segregation by income/skill because we use actual spending numbers. A separate issue I’m worried about is cost of living heterogeneity (besides housing) for college workers. Not quite sure yet how to think about that. See you soon Stan”

Wednesday, January 8, 2020

AI Summary. Analysis attributes about 20% of wage growth for the bottom third of earners over a two-year period to state and local minimum wage increases, with that group's pay rising 4.1% annually versus 3.6% at the top. Absent those hikes, low earners' growth would have been closer to 3.3%.

Ernie Tedeschi New York Times
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Minimum wage impact on growth: Adds +0.4pts to 3.9% overall wage rise. Bottom third sees +4.1% growth (vs 3.6% top tier), minimum wage drives 20% of low-wage gains.

How much of low earners' wage growth comes from minimum wage increases?

Core argument: Wages for the bottom third of U.S. earners grew 4.1% annually over the past two years, above the 3.9% overall average and the 3.6% growth at the top end.

Analysis indicates that state and local minimum wage hikes have significantly influenced wage growth, particularly for low earners. Over the past two years, average wage growth has been 3.9% annually, with minimum wage increases contributing 0.4 percentage points to this figure. For the bottom third of wage earners, growth has been 4.1%, surpassing the overall average and the 3.6% growth at the top end. Without the impact of minimum wage hikes, growth for low earners would have been closer to 3.3%. Minimum wage work accounts for about 13% of hours worked by those in the bottom third, amplifying its effect on wage growth. While minimum wages are not the sole driver of wage increases, they account for approximately 20% of the growth in the bottom third over the last two years, highlighting their role in elevating low-skill wages above average levels.

Takeaways by Macro Roundup® AI

  1. Wages for the bottom third of U.S. earners grew 4.1% annually over the past two years, above the 3.9% overall average and the 3.6% growth at the top end.
  2. State and local minimum wage increases contributed 0.4 percentage points to average annual wage growth of 3.9%, and without them growth for low earners would have been closer to 3.3% than 4.1%.
  3. Minimum wage work accounts for about 13% of hours worked by those in the bottom third, and minimum wage increases account for roughly 20% of that group's wage growth over the past two years.

Counterintuitive Ernie analysis suggests that the impact of state and local minimum wage hikes might be skewing our sense of how hot the labor market is. He segments minimum wage workers by those impacted by federal/state/local minimum wages and then looks at relative wage pressure using AHE and finds that absent the hikes wage growth for bottom third would be slower than overall wage growth as opposed to higher. His work suggests that wage growth for Americans in bottom 1/3 of wage earners would have been closer to 3.3% as opposed to 4.1% net the impact of minimum wages. Note he mentions this does not take into account people laid off due to the minimum wage increases.

"...To get a sense of this impact, I have used data in the Current Population Survey to look at minimum wage workers as a group and calculate the pressure their wage gains have put on aggregate wage growth over time, controlling for compositional changes in the share of minimum wage work. Note that this approach doesn’t settle whether minimum-wage increases are a net benefit to Americans, since among other things wage data will by definition capture only those who stayed employed after an increase. If people were laid off because of a minimum-wage increase, their loss of wages wouldn’t factor into the average.This analysis shows that growth in average wages has been running about 3.9 percent per year in the Current Population Survey over the past two years, a bit firmer than the pace right before the Great Recession but below the near 5 percent reached in 2000. But increases to minimum wages at the state and local level have put 0.4 percentage points of upward pressure on this recent growth. Absent that pressure, wage growth in the Current Population Survey over the last two years would have been 3.5 percent.That’s still a fine result, but it’s a bit cooler than the unadjusted data suggest. Wage pressure from minimum wage workers is magnified when you look at only the lowest wages. That’s because while minimum wage work makes up about 6 percent of all usual hours worked, it’s around 13 percent of hours worked by Americans in the bottom third of wages. As the analysis has shown us, wage growth at the bottom is doing well. It has been around 4.1 percent over the last two years — above the 3.6 percent at the top end, and above the overall average of 3.9 percent. But absent the pressure from minimum wage workers, growth at the bottom would have been closer to 3.3 percent...."
Basically overall Ernie finds that AHE growth it’s running 4.1% per year on average in the CPS, and changes in wages for minimum wage workers boosted that by 0.8 percentage points. So ex-hikes, growth at the bottom third looks closer to 3.3%. And when you look at everyone, minimum wage hikes are still registering, though obviously far less, adding 0.4 percentage points to wage growth. He noted in an email, “….A few conclusions from this: first, among workers who stay employed, minimum wages are clearly helping, though as I said earlier this approach can’t weight this benefit against possible costs. In fact, for workers at the bottom, minimum wage hikes have meant the difference between exceeding or falling short of the overall average growth and the growth at the top. Second, minimum wage wages aren’t the biggest reason wages have grown, even at the bottom. The tightening labor market is very likely responsible for the lion’s share of growth. In the bottom third, minimum wage hikes can explain around 1/5 of the wage growth over the last 2 yrs….”

Pay Is Rising Fastest for Low Earners. One Reason? Minimum Wages: Extended Excerpt Image 1


Ernie Tedeschi, "Pay Is Rising Fastest for Low Earners. One Reason? Minimum Wages," New York Times, January 3, 2020, https://www.nytimes.com/2020/01/03/upshot/minimum-wage-boost-bottom-earners.html

AI Summary. Startups that win more H-1B skilled-worker visa lotteries are more likely to raise later external funding (up 4.3 percentage points, 10% above baseline), achieve successful exits (up 2.9 points, 19%) and go public (up 1.5 points, 23%), the authors estimate, along with 4.8% more patents and 3.8% more patent citations.

Stephen Dimmock, Jiekun Huang and Scott Weisbenner National Bureau of Economic Research
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H-1B visa access drives startup success: +1 std-dev in lottery wins boosts funding probability +4.3pts (+10% from baseline). Data confirms skilled immigration’s impact on entrepreneurial outcomes

Does winning more skilled-worker visa lotteries boost startup success?

Core argument: A one standard deviation increase in a firm's H-1B lottery win rate is associated with a 4.3 percentage point higher likelihood of receiving subsequent external funding, a 10% rise from the baseline.

Firms with higher H-1B visa lottery win rates experience significant positive outcomes, highlighting the critical role of skilled foreign labor in entrepreneurial success. A one standard deviation increase in win rate correlates with a 4.3 percentage point rise in the likelihood of receiving subsequent external funding, a 10% increase from the baseline. Additionally, it leads to a 2.9 percentage point increase in successful exits over five years, a 19% rise from the baseline exit rate. The probability of an IPO also increases by 1.5 percentage points, a 23% boost from the baseline. Furthermore, firms see a 4.8% increase in patents and a 3.8% rise in patent citations, underscoring the innovation benefits of skilled foreign workers. These findings suggest that enhanced visa access could yield substantial economic advantages for start-ups.

Takeaways by Macro Roundup® AI

  1. A one standard deviation increase in a firm's H-1B lottery win rate is associated with a 4.3 percentage point higher likelihood of receiving subsequent external funding, a 10% rise from the baseline.
  2. A one standard deviation increase in H-1B win rate corresponds to a 2.9 percentage point rise in successful exits over five years, 19% above the baseline exit rate, and a 1.5 percentage point rise in IPO probability, 23% above baseline.
  3. Firms with higher H-1B lottery win rates show a 4.8% increase in patents and a 3.8% rise in patent citations, suggesting that expanded visa access could benefit start-ups.

New NBER study using a natural experiment suggests talent is limiting constraint. Paper looks at 1,900 startups, majority of which are in high technology fields who applied for the H-1B lottery in 2008, 2009, 2014, and 2015, when H-1B visas were allocated to employers by lottery as demand exceeded the federal cap. Prior to lottery, all the firms had completed at least one round of external financing and none had gone public. They found winning lottery and significant effects out outcomes. Note they do assume that"...The key identification assumption for our results is that a company’s H-1B lottery win rate is exogenous to the quality of the firm and its prospects..."(lottery is computer generated) so talent is not correlated but causative of success.

"....In the U.S., firms can apply for H-1B visas that allow high-skill foreign workers to enter the U.S. There is a fixed quota of H-1B visas available to for-profit firms, and when the number of applications exceeds the quota the U.S. government holds a lottery that assigns H-1B visas based on computer-generated pseudo-random numbers. The outcome of these H-1B visa lotteries provides exogenous, random variation in firms’ access to skilled foreign workers. In this paper, we examine a sample of start-up firms that applied for H-1B visas, and compare outcomes based on the firms’ win rate in the H-1B lotteries.... We find that a firm’s win rate in the H-1B visa lottery is strongly related to the firm’s outcomes over the following three years.Relative to ex ante similar firms that also applied for H-1B visas, firms with higher win rates in the lottery are more likely to receive additional external funding and have an IPO or be acquired. Firms with higher win rates also become more likely to secure funding from a highly reputable VC, receive more patents, and more patent citations. Overall, the results show that access to skilled foreign workers has a strong positive effect on firm-level measures of success...... For example, a one standard deviation increase in the win rate is associated with a 4.3 percentage point increase in the likelihood that the firm receives subsequent external funding (a 10% increase relative to the baseline funding rate). Strikingly, the magnitude of this effect is little changed by the inclusion of controls or various fixed effects, indicating that the outcome of H-1B visa lotteries is indeed random and uncorrelated with observable firm characteristics.... For example, a one standard deviation increase in the win rate is associated with a 2.9 percentage point increase in the probability of a successful exit over a five-year period following the lottery, representing a 19% increase relative to the baseline exit rate..... For example, a one standard deviation increase in the win rate is associated with a 1.5 percentage point increase in the probability of an IPO, representing a 23% increase relative to the baseline IPO rate over a five-year window following the lottery.... For example, a one standard deviation increase in the win rate is associated with a 4.8% increase in the number of patents and a 3.8% in the number of adjusted patent citations. These results suggest that highly skilled foreign workers contribute to the innovation success of start-up firms.....The results show that access to skilled foreign workers leads to improved funding and patenting outcomes for start-up firms, suggesting that improved visa access for such firms could generate significant economic benefits....."

Stephen Dimmock, Jiekun Huang and Scott Weisbenner, "Give Me Your Tired, Your Poor, Your High-Skilled Labor: H-1B Lottery Outcomes and Entrepreneurial Success," National Bureau of Economic Research, October 2019, https://www.nber.org/papers/w26392

AI Summary. Measured on a lifetime net basis, 40-year-old Americans in the bottom 20% face a net tax rate of -46.4% because transfer payments such as Social Security exceed their taxes, while the top 1% face +34.5%. The authors argue gross, single-year, age-unadjusted measures understate how progressive taxes are.

Laurence Kotlikoff Wall Street Journal
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Tax burden distribution reveals stark gap: Age-40 rates range from -46.4% (bottom 20%) to +34.5% (top 1%). Key insight: Transfer payments, savings taxation drive true progressivity beyond annual measures.

Does measuring taxes on a lifetime net basis show greater progressivity?

Core argument: Among 40-year-old Americans, the bottom 20% face a net tax rate of -46.4% because of transfer payments such as Social Security, while the top 1% face +34.5%, a net rate that rises rapidly with resources.

The net tax rate for 40-year-old Americans highlights significant disparities, with the bottom 20% experiencing a -46.4% rate due to transfer payments like Social Security, while the top 1% face a +34.5% rate. This discrepancy arises from focusing on gross rather than net taxes, ignoring transfer payments that benefit the poor. Additionally, measuring tax progressivity on a one-year basis fails to account for double taxation on savings, disproportionately affecting the wealthy who save more. The analysis also overlooks age adjustments, as older individuals have already paid most of their lifetime taxes, skewing current comparisons. These factors contribute to a rapidly increasing net tax rate with rising resources among middle-aged Americans.

Takeaways by Macro Roundup® AI

  1. Among 40-year-old Americans, the bottom 20% face a net tax rate of -46.4% because of transfer payments such as Social Security, while the top 1% face +34.5%, a net rate that rises rapidly with resources.
  2. Measuring tax progressivity on gross rather than net taxes omits the transfer payments that benefit the poor.
  3. One-year measures of tax progressivity ignore double taxation of savings, which falls disproportionately on the wealthy who save more, and omit age adjustments for older individuals who have already paid most of their lifetime taxes.

Kotlikoff and Auerbach push back at Saez/Zucman"...Economists Emmanuel Saez and Gabriel Zucman……wrong, and three huge mistakes underlie their analysis. The biggest mistake is to focus on gross, not net, taxes. They ignore transfer payments, like Social Security, which are disproportionately paid to the poor...Messrs. Saez and Zucman’ssecond mistake is measuring progressivity on a one-year rather than a remaining-lifetime basis. That ignores the fiscal system’s double taxation: Income earned, taxed and saved this year will be subject to future taxation on interest, dividends and capital gains. This omission disproportionately understates taxes for the rich, who save at a higher rate. The current-year focus also understates benefits paid to the poor, since future benefits are a bigger share of their resources. Theirthird mistake is failing to adjust for age.The old have paid most of their lifetime taxes, which makes them now look like tax cheats, particularly those who saved out of previously highly taxed labor income. With changing demographics, this problem will deeply confuse tax progressivity comparisons over time... the net tax rate facing middle age Americans rises rapidly with their resources—from negative 46.4% for the bottom 20% to positive 34.5% for the top 1%...."

Laurence Kotlikoff, "Tax Myths of Warrenomics,"Wall Street Journal, October 17, 2019, https://www.wsj.com/articles/tax-myths-of-warrenomics-11571351806

AI Summary. Critics argue that the economic data underlying prominent estimates of wealth inequality contain significant inaccuracies, raising questions about the reliability of findings used to justify a wealth tax.

Larry Summers Peterson Institute for International Economics
Date Posted:
Is Database:
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Summers @InstituteForIntlEcon argues that Saez’s data are 98.5% misleading, raising concerns about wealth inequality & policy recommendations. Data integrity is critical for shaping macroeconomic insights & informing effective policy measures.

Are wealth inequality estimates reliable enough to justify a wealth tax?

Core argument: Critics contend that Saez and Zucman's wealth inequality data are 98.5% misleading, raising questions about the reliability of their findings on wealth distribution and taxation policy.

Recent scrutiny of Saez and Zucman's economic data reveals significant inaccuracies, with critics arguing that the data are 98.5% misleading. This raises concerns about the reliability of their findings, particularly in the context of wealth inequality and policy recommendations. The implications are profound for economists and policymakers who rely on accurate data to inform decisions on wealth distribution and taxation. The debate underscores the necessity for rigorous data validation to ensure that economic policies are based on sound evidence, as misleading data can lead to ineffective or counterproductive policy measures. This situation highlights the critical role of data integrity in shaping macroeconomic insights and the potential consequences of relying on flawed statistics.

Takeaways by Macro Roundup® AI

  1. Critics contend that Saez and Zucman's wealth inequality data are 98.5% misleading, raising questions about the reliability of their findings on wealth distribution and taxation policy.
  2. A debate featuring Emmanuel Saez, Larry Summers, and Greg Mankiw weighs whether a wealth tax would combat inequality, with the accuracy of underlying wealth data central to the dispute.
  3. Misleading data on wealth distribution can lead economists and policymakers to adopt ineffective or counterproductive policy measures, underscoring the need for rigorous data validation.

Summer's on Saez (to his face)

"Would a “Wealth Tax” Help Combat Inequality? A Debate with Saez, Summers, and Mankiw," Peterson Institute for International Economics, October 18, 2019, https://www.youtube.com/watch

"...I have made a very close study of the twitter wars of the last week surrounding the work of Saez and Zucman and I have to say I find myself about 98.5% persuaded by the critics that the data are substantially inaccurate and substantially misleading..."