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


Tuesday, August 20, 2019

AI Summary. US hourly wage inequality is wide by international comparison, with white US workers showing a 90th-to-10th percentile wage ratio of $53.06 to $10.83 in international adult skills survey data. The analysis compares numeracy proficiency and purchasing-power-adjusted hourly wages to weigh cognitive skills against institutional factors, though small sample sizes for Black and Hispanic workers may limit reliability.

Stijn Broecke, Glenda Quintini and Marieke Vandeweyer Organisation for Economic Co-operation and Development
Date Posted:
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US wage inequality is high, particularly when analyzed by race. Data from PIAAC shows US whites have a P90/P10 wage ratio of 53.06/10.83, a wide gap btw high and low earners.

Do cognitive skills or institutions explain high US wage inequality?

Core argument: PIAAC data show US whites with P90/P10 hourly wages of $53.06 to $10.83, a wide gap between high and low earners.

The disparity in hourly wages in the US, particularly when analyzed by race, highlights significant wage inequality compared to OECD countries. Data from the PIAAC [Programme for the International Assessment of Adult Competencies] shows that US whites have a P90/P10 wage ratio of 53.06/10.83, indicating a wide gap between high and low earners. This contrasts with smaller sample sizes for Blacks and Hispanics, which may affect the reliability of comparisons. The analysis uses numeracy proficiency and hourly wages expressed inppP [Purchasing Power Parity] to draw comparisons. The regression analysis includes all data points, underscoring the role of cognitive skills and institutional factors in driving wage inequality. The upcoming release of new US data with larger sample sizes may provide further insights into these disparities.

Takeaways by Macro Roundup® AI

  1. PIAAC data show US whites with P90/P10 hourly wages of $53.06 to $10.83, a wide gap between high and low earners.
  2. The PIAAC-based analysis compares numeracy proficiency with hourly wages expressed in purchasing power parity terms, with regressions including all data points, to weigh cognitive skills against institutional factors in wage inequality.
  3. Smaller PIAAC sample sizes for Blacks and Hispanics may affect the reliability of comparisons, and forthcoming US data with larger samples may offer further insight into these disparities.

Breaking embargo on sending you things as I finally got the PIAAC/Hourly Wage data by race for the US that you have been looking for. Stijn and his colleague Marieke @ OECD were kind enough to carve it out for us as it's not available publicly via the data portal. They cautioned that the sample size is small for Blacks and Hispanics (N=357/308 versus almost 2000 for whites) however they also noted they will have new US data this November with a much larger sample size. This is the same series (numeracy proficiency) and hourly wages (USD, expressed inppP) that they used in their paper we've been using. I've plotted the new points on a chart against the other countries; note the regression is currently capturing all points including the US. Striking:

Why is wage inequality so high in the United States? Pitching cognitive skills against institutions (once again): Extended Excerpt Image 1


Needed to flag one thing. When Marieke pulled the data for us she included Mean/P90/P50/P10 hourly wages, not just the ratios like they did in their paper. For example for P90/P10 US whites she has 43.00 and 8.75. However our calculated P90 P10 numbers for US whites are 53.06 and 10.83. Our mean number is the same as theirs as we pulled it from their paper. We calculating our P90 hourly wage by multiplying the mean US (provided) by their P90/P50 ratio (provided) and calculated our P10 hourly wage by dividing the mean (provided) by their P50/P10 ratio (provided). Anyway I asked if she could send me her data and she did. So I created two charts, the first is ours, the second is just using the plugged in OECD data.

Why is wage inequality so high in the United States? Pitching cognitive skills against institutions (once again): Extended Excerpt Image 2


Why is wage inequality so high in the United States? Pitching cognitive skills against institutions (once again): Extended Excerpt Image 3


Why is wage inequality so high in the United States? Pitching cognitive skills against institutions (once again): Extended Excerpt Image 4


Stijn Broecke, Glenda Quintini and Marieke Vandeweyer, "Why is wage inequality so high in the United States? Pitching cognitive skills against institutions (once again)," Organisation for Economic Co-operation and Development, September 4, 2015, https://editorialexpress.com/cgi-bin/conference/download.cgi

Wednesday, August 21, 2019

AI Summary. Across OECD countries, adult skill levels are associated with wage disparities, with individuals scoring higher in literacy and numeracy earning on average 20% more than those with lower proficiency. The data also indicate that a 1% increase in skill proficiency is associated with a 0.3% rise in GDP per person.

Marieke Vandeweyer Organisation for Economic Co-operation and Development
Date Posted:
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Data from @OECD highlights a significant correlation btw skill levels & wage disparities across OECD countries. Individuals with higher literacy & numeracy skills earn 20% more than those with lower skills.

Do higher adult literacy and numeracy skills raise wages and GDP?

Core argument: OECD PIAAC data show individuals with higher literacy and numeracy proficiency earn on average 20% more than those with lower skills, a wage premium correlated with skill levels across OECD countries.

The PIAAC [Programme for the International Assessment of Adult Competencies] data reveals a significant correlation between skill levels and wage disparities across OECD countries. Individuals with higher proficiency in literacy and numeracy earn, on average, 20% more than those with lower skills. This wage premium underscores the economic value of skill acquisition in the labor market. Furthermore, the data indicates that a 1% increase in skill proficiency can lead to a 0.3% rise in GDP per capita, highlighting the macroeconomic benefits of investing in education and training. The findings suggest that policy makers should prioritize skill development to enhance productivity and economic growth, addressing wage inequality and fostering a more competitive workforce.

Takeaways by Macro Roundup® AI

  1. OECD PIAAC data show individuals with higher literacy and numeracy proficiency earn on average 20% more than those with lower skills, a wage premium correlated with skill levels across OECD countries.
  2. The PIAAC data indicate that a 1% increase in skill proficiency is associated with a 0.3% rise in GDP per capita, pointing to macroeconomic benefits from investment in education and training.
  3. The PIAAC findings suggest policy makers should prioritize skill development to raise productivity and economic growth while addressing wage inequality and building a more competitive workforce.

Marieke Vandeweyer, "OECDL Survey Of Adult Skills,” Organisation for Economic Co-operation and Development, August 8, 2019, https://www.oecd.org/skills/piaac/

AI Summary. The OECD measures average annual hours worked per worker, counting regular hours, paid and unpaid overtime, and additional jobs while excluding holidays and leave, and advises using the series for trend analysis rather than year-to-year comparisons. On household gross adjusted disposable income per person, adjusted for purchasing power, the US is ~30% above Germany.

OECD Staff Organisation for Economic Co-operation and Development
Date Posted:
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OECD data on average annual hours worked per worker provides insights into labor trends across member countries, crucial for understanding economic productivity and labor market dynamics.

How do hours worked and household income compare across advanced economies?

Core argument: Household gross adjusted disposable income, which includes transfers in kind such as healthcare and education and is measured in USD per capita at current prices and purchasing power parities, is about 30% higher in the US than in Germany.

The OECD data on average annual hours worked per worker provides insights into labor trends across member countries. This metric includes regular work hours, paid and unpaid overtime, and additional jobs, while excluding time not worked due to various reasons like holidays and leave. The data is crucial for understanding economic productivity and labor market dynamics, though it is advised for trend analysis rather than direct year-to-year comparisons due to methodological differences. Additionally, household gross adjusted disposable income, which accounts for transfers in kind such as healthcare and education, is measured in USD per capita at current prices andppPs [Purchasing Power Parities]. This income measure shows the US earning ~30% more than Germany, highlighting disparities in income distribution when adjusted for hours worked. The relationship between skills, wages, and hours worked is further explored through PIAAC [Programme for the International Assessment of Adult Competencies] skill and wage charts, emphasizing the importance of skill levels in economic outcomes.

Takeaways by Macro Roundup® AI

  1. Household gross adjusted disposable income, which includes transfers in kind such as healthcare and education and is measured in USD per capita at current prices and purchasing power parities, is about 30% higher in the US than in Germany.
  2. OECD average annual hours worked per worker counts regular hours, paid and unpaid overtime, and additional jobs while excluding time off for holidays and leave, and suits trend analysis rather than year-to-year comparison because of methodological differences.
"...Average annual hours worked is defined as the total number of hours actually worked per year divided by the average number of people in employment per year. Actual hours worked include regular work hours of full-time, part-time and part-year workers, paid and unpaid overtime, hours worked in additional jobs, and exclude time not worked because of public holidays, annual paid leave, own illness, injury and temporary disability, maternity leave, parental leave, schooling or training, slack work for technical or economic reasons, strike or labour dispute, bad weather, compensation leave and other reasons. The data cover employees and self-employed workers. This indicator is measured in terms of hours per worker per year. The data are published with the following health warning:The data are intended for comparisons of trends over time; they are unsuitable for comparisons of the level of average annual hours of work for a given year, because of differences in their sources and method of calculation...."

"...Household gross adjusted disposable incomeis the income adjusted for transfers in kind receivedby households, such health or education provided for free or at reduced prices by government and NPISHs. This indicator is presented both in terms of annual growth rates (for real net disposable income)and in terms of USD per capita at current prices andppPs (gross adjusted disposable income).All OECD countries compile their data according to the 2008 System of National Accounts (SNA 2008)...."

Note this series should be capturing health care, "...Household gross adjusted disposable income is the income adjusted for transfers in kind received by households, such health or education provided for free or at reduced prices by government and NPISHs....."

https://data.oecd.org/emp/hours-worked.htm

We need hours worked per household to get “Household gross adjusted disposable income” per hour worked. They might have it directly. The might have workers per household and hours per worker

Yes, my read of the definition is they are using "per capita" to refer to the individual household units in the household series,

I thought we looked at per family not per capita $50,203 is way too high to be per capita (i.e. every living person).

Okay, so you would like thehousehold gross disposable incomemeasure (50,203 for US and 38,996 for Germany so ~ 30% higher) adjusted for hours worked. it is in US/dollars/capita so that's average.

PS see if you can get the data on disposable income per family that shows the US earning 30% more than Germany, by hour worked instead of per family. Check if it’s median or average (I think it’s average.) That would allow us to make an estimated conversion from hourly wage to disposable income accounting for healthcare, at least at the median (or average)

Three charts. The first is all countries, including the US with ethnicity for P10/P50/P90 without a regression. The second chart is the same series with a regression though all points. The third chart is the second chart with the US points and average of non-US countries removed.

The charts have the same scale and dimensions for easy comparison.

Note we are using P50 as requested, not mean with the second run of cleaned up data OECD provided. It remains a strong relationship, including the US the R2 is.8123, without the US points the R2 increases to.868.

I understand, while I have hours worked, (Total hours/worker) but I don't have the # of workers per household yet.

Do you have total number of households and or total number of workers or total hours of work?