Edward Conard

Top Ten New York Times Bestselling Author

  • “…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 must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…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
  • “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
  • “…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
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Wednesday, May 4, 2022

AI Summary. Patent data spanning 1842 to 2022 tracks successive technological revolutions, with railroads dominating the early 1800s, steel and mass electrification around 1900, automobiles in the 1920s, circuitry from the late 1950s, and the internet in the late 1990s. Clustering patents into broad categories and tracking changes in each category's share of patents identifies trending technologies.

Kai Wu Sparklline Capital
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Patent data highlights key technological revolutions from 1842 to 2022, with railroads, steel, automobiles, and internet driving economic transformation.

Does patent data reveal successive technological revolutions and emerging trends?

Core argument: Patent data spanning 1842–2022 tracks successive technological revolutions, with railroads dominating the early 1800s and peaking again around 1900 during steel retrofitting.

The evolution of technology from 1842 to 2022 is vividly reflected in patent data, highlighting key technological revolutions. Railroads dominated the early 1800s, peaking again with steel retrofitting around 1900. The Age of Steel aligned with mass electrification, while the 1920s saw the automobile transform infrastructure. The late-1950s marked the onset of the Age of Information with circuitry, further accelerated by the internet in the late-1990s. By clustering patents into broad categories, we observe shifts in patent shares, focusing on the rate of change to identify trending technologies. This approach reveals how innovations like the internal combustion engine and internet have driven economic transformation, underscoring the importance of investing in emerging technologies to sustain growth and competitiveness.

Takeaways by Macro Roundup® AI

  1. Patent data spanning 1842–2022 tracks successive technological revolutions, with railroads dominating the early 1800s and peaking again around 1900 during steel retrofitting.
  2. The Age of Steel coincided with mass electrification, the 1920s automobile reshaped infrastructure, and the late-1950s rise of circuitry began the Age of Information, accelerated by the internet in the late-1990s.
  3. Clustering patents into broad categories and tracking the rate of change in patent shares identifies trending technologies, with innovations such as the internal combustion engine and the internet presented as drivers of economic transformation.

Amazingly, we see these technological revolutions play out in the patent data.The next exhibit uses our NLP model....extract topics directly from patent text using natural language processing (NLP). We use the embeddings from Investment Management in the Machine Learning Age...We train our model on textual data in the title, abstract, and description. We include bigrams and trigrams to capture phrases such as “internal combustion engine.”... to cluster patents into broad categories (e.g., the automobile) and plots the share of patents in each. Railroads were the technological marvel of the early 1800s, with a secondary peak at the turn of the 20th century as railways were retrofitted with steel. The Age of Steel also coincided with the mass electrification of the economy. In the 1920s, the automobile revolutionized our infrastructure. In the late-1950s, circuitry ushered in the Age of Information, which accelerated further with the internet in the late-1990s. We can drill down into the individual technologies powering each revolution (e.g., “internal combustion engine” instead of “automobile”). Rather than absolute levels, we focus on the rate of change in order to find “trending technologies.” Exhibit 10 shows top trending technologies by decade.

Kai Wu, "Investing in Innovation,"Sparklline Capital, April 2022, https://www.sparklinecapital.com/post/investing-in-innovation

AI Summary. The authors estimate that a 10% increase in a state's effective minimum wage is associated with a 2.4% decline in job vacancies in the same quarter, and a 4.5% decline a year later in at-risk occupations where many workers earn at or near the minimum wage. Vacancies in low-education roles fall 3.8%.

Marianna Kudlyak Federal Reserve Bank Of Cleveland
Date Posted:
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A 10% increase in state-level effective minimum wage reduces vacancies by 2.4% in the same quarter & 4.5% a year later in at-risk occupations.

Do minimum wage increases reduce job openings for low-wage workers?

Core argument: Kudlyak, Tasci, and Tüzemen find that a 10% increase in the state-level effective minimum wage reduces job vacancies by 2.4% within the same quarter, with a 4.5% decline a year later in at-risk occupations.

A 10% increase in the state-level effective minimum wage leads to a 2.4% reduction in job vacancies within the same quarter, with a more pronounced 4.5% decrease observed a year later in at-risk occupations. These occupations are characterized by a significant proportion of workers earning at or near the minimum wage, often requiring lower educational attainment and prevalent in areas with higher poverty rates. The decline in vacancies is attributed to firms' preemptive measures to cut job openings in anticipation of wage hikes. This effect is particularly evident in low-education roles, where vacancies drop by 3.8% following a minimum wage increase. The study highlights the differential impact of wage policy changes across various occupational sectors, emphasizing the vulnerability of low-wage workers to such economic shifts.

Takeaways by Macro Roundup® AI

  1. Kudlyak, Tasci, and Tüzemen find that a 10% increase in the state-level effective minimum wage reduces job vacancies by 2.4% within the same quarter, with a 4.5% decline a year later in at-risk occupations.
  2. Vacancies in low-education roles fall 3.8% following a minimum wage increase, with at-risk occupations defined by large shares of workers earning at or near the minimum wage and concentration in higher-poverty areas.
  3. Kudlyak, Tasci, and Tüzemen attribute the vacancy decline to firms cutting job openings preemptively in anticipation of wage increases.

We use a unique data set and propose a novel identification strategy to estimate the impact of minimum wage increases on labor demand as measured by vacancies, a labor market outcome variable that has not been studied in the large empirical minimum wage literature. Our identification strategy builds on the idea that not all occupations are similarly affected by minimum wage increases. A much larger fraction of workers earn at or near the prevailing minimum wage level in some occupations than in others. Intuitively, one would expect these occupations to be affected differently by a minimum wage increase. We formalize this idea by identifying six 2-digit occupations as potentially at-risk occupations....First, we designate workers who earn at or below 110 percent of the effective state-level minimum wage as those who earn close to the minimum wage. The 110 percent threshold is partly informed by the distribution of the minimum wage increases shown in Figure 3. Second, we consider an occupation to be in the at-risk group, if during the entire sample period, the fraction of workers earning at or below 110 percent of the effective minimum wage is at least 5 percent. In Section 6, we show that our results are robust to variations in these two threshold level...Our results show statistically significant and large negative effects of minimum wage increases on vacancies in at-risk occupations. More specifically, vacancies in at-risk occupations drop by 2.4 percent in response to a 10 percent rise in the prevailing minimum wage relative to other occupations. This baseline result is driven by a strong preemptive response by firms cutting vacancies in advance of the minimum wage change. Furthermore, the decline in vacancies is larger for occupations that generally employ workers with lower educational attainment and those in counties with higher poverty rates....Workers who are more susceptible to the adverse effects of minimum wage hikes are naturally low-wage workers. If low-wage workers are more likely to be employed in occupations that do not require high levels of educational attainment, then we might expect to see more adverse effects from minimum wage increases on these occupations. In Column (3) of table 5, we see that this is indeed the case. We find that a 10 percent minimum wage increase reduces low-education vacancies by about 3.8 percent.

At-risk occupations

"...We designate an occupation as an “at-risk occupation” if a large share of workers in the occupation earn at or close to the effective minimum wage...To identify at-risk occupations, we construct hourly wage distributions of the occupations at the two-digit level by state using micro-level data from the CPS. To construct these distributions, we use hourly wage information (from the fourth and eighth months interviews in the CPS). When hourly wage data are not available, we use weekly hours worked and weekly earnings to compute an hourly wage. We restrict our sample to employed individuals of age 16 and older, and exclude those who are self-employed or work without pay....First, we designate workers who earn at or below 110 percent of the effective state-level minimum wage as those who earn close to the minimum wage. The 110 percent threshold is partly informed by the distribution of the minimum wage increases shown in Figure 3. Second, we consider an occupation to be in the at-risk group, if during the entire sample period, the fraction of workers earning at or below 110 percent of the effective minimum wage is at least 5 percent. In Section 6, we show that our results are robust to variations in these two threshold levels..."

Vacancy data

"...The vacancy data in our analysis come from the job openings data from the Conference Board as a part of its Help Wanted OnLine (HWOL) data series..."

"...Workers who are more susceptible to the adverse effects of minimum wage hikes are naturally low-wage workers. If low-wage workers are more likely to be employed in occupations that do not require high levels of educational attainment, then we might expect to see more adverse effects from minimum wage increases on these occupations. In Column (3) of table 5, we see that this is indeed the case We find that a 10 percent minimum wage increase reduces low-education vacancies by about 3.8 percent...."

Marianna Kudlyak, Murat Tasci, and Didem Tüzemen, "Minimum Wage Increases and Vacancies,"Federal Reserve Bank Of Cleveland Working Paper Series, April 2022https://www.clevelandfed.org/en/newsroom-and-events/publications/working-papers/2022-working-papers/wp-1930r-minimum-wage-increases-and-vacancies.aspx

The Evidence

Thursday, May 5, 2022

AI Summary. Using international student and adult skills assessments to build a macroeconomic measure of human capital, the authors estimate that the stock of human capital is 3-4x more responsive to education quality than to years of schooling. The measure is correlated with productivity, though less so over time within OECD countries.

Balázs Égert, Christine de la Maisonneuve and David Turner OECD
Date Posted:
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Education quality outweighs quantity for growth: PISA/PIAAC data shows 3-4x stronger elasticity vs years studied. Quality improvements drive productivity gains more than increased education years.

Does education quality matter more than years of schooling for human capital?

Core argument: The elasticity of the human capital stock with respect to education quality is 3–4 times larger than with respect to education quantity, according to a measure built from PISA and PIAAC surveys.

The new macroeconomic measure of human capital, utilizing PISA [Programme for International Student Assessment] and PIAAC [Programme for the International Assessment of Adult Competencies] surveys, reveals that the elasticity of human capital stock with respect to education quality is 3-4 times larger than for education quantity. This measure, a cohort-weighted average of past PISA scores and mean years of schooling, shows a strong link to productivity, suggesting that improvements in education quality could yield significant productivity gains. Among 53 OECD and non-OECD countries, Australia, Japan, and Finland rank highest in human capital, while Chile and Thailand rank lowest. The correlation between human capital and productivity is evident, although less pronounced over time within OECD countries. The findings highlight the potential for education policy reforms to enhance human capital and drive economic growth, with the impact of quality improvements being more substantial than quantity increases.

Takeaways by Macro Roundup® AI

  1. The elasticity of the human capital stock with respect to education quality is 3–4 times larger than with respect to education quantity, according to a measure built from PISA and PIAAC surveys.
  2. The measure, a cohort-weighted average of past PISA scores and mean years of schooling, correlates strongly with productivity, though less markedly over time within OECD countries.
  3. Among 53 OECD and non-OECD countries, Australia, Japan, and Finland rank highest in human capital, while Chile and Thailand rank lowest.

“….This paper provides a new measure of human capital using PISA and PIAAC surveys, and mean years of schooling. The new measure is a cohort-weighted average of past PISA scores (representing the quality of education) of the working age population and the corresponding mean years of schooling (representing the quantity of education). In contrast to the existing literature, the relative weights of each component are not imposed or calibrated but directly estimated. The paper finds that the elasticity of the stock of human capital with respect to the quality of education is three to four times larger than for the quantity of education. The new measure has a strong link to productivity with the potential for productivity gains being much greater from improvements in the quality than quantity component of human capital. The magnitude of these potential gains in MFP is comparable to a similarly standardised improvement in product market regulation, but the effects materialise with much longer lags. The paper demonstrates through the example of pre-primary education, how to simulate the impact of a particular reform to education policy on human capital and productivity….”

Balázs Égert, Christine de la Maisonneuve and David Turner, "A new macroeconomic measure of human capital exploiting PISA and PIAAC: Linking education policies to productivity,"OECD, April 2022, https://www.oecd-ilibrary.org/docserver/a1046e2e-en.pdf

“.. The newly constructed stock of human capital appears to be positively correlated with productivity for a cross-section of 53 OECD and non-OECD countries (Figure 5, Panel A), although this is somewhat less apparent when comparing time series observations for just OECD countries (Figure 5, Panel B).The strength of the correlation is better assessed through the estimation of an empirical relationship linking multifactor productivity to human capital, controlling for other determinants…”

Relationship Btw Measure Of Human Capital And Productivity

“…. The new measure of human capital for the population aged 16 to 65 years suggests that the countries with the greatest human capital include Australia, Japan and Finland, while Chile and Thailand have the lowest levels of human capital among this group of 16 countries (Figure 4, Panel A).These rankings for the last available year are broadly confirmed for the stock covering the population aged 16-39 (Figure 4, Panel B), although there are some differences suggesting that younger generations have been increasingly better educated in some countries than in others.For instance, Finland lags behind Japan for the total stock, but the countries flip places for the stock covering only the younger generations, because the stock of human capital of younger generations has risen more strongly in Finland compared to Japan. More generally, an ageing population might act as a drag on total human capital because of their typically lower student test scores and a lower number of years spent in education and also because of the declining pass-through from student test scores to adult test scores(as represented by the coefficient estimates of𝜎 and𝜃 in Table 4)…”

“….For the countries for which bot…can be calculated, the two measures show a high correlation (Figure 2)…”

“..To capture quantity aspects, mean years of schooling (MYS) are added, on top of the student test scores, as an explanatory variable to the adult test score regressions…”

Cutting in “Quantity” Of Education

“..An increase in the years spent in the education system boost adult test scores in a statistically significant manner (Table 4, equations and ), although contrary to student test scores, the positive effect of mean years of schooling on adult test scores does not decline for older cohorts. The elasticity of cohort-specific adult skills with respect to student test scores is three to four times higher than the elasticity with respect to mean years of schooling. This result can be contrasted with the measures of learning adjusted years of schooling, which have been used in the literature but by construction usually impose the same elasticity for the quantity and quality components.Furthermore, this result can be used to combine the quality and quantity measures of education in a single stock measure of human capital…”

Regression Results

“…A building block of the new measure of human capital is establishing a link between PIAAC adult test scores and student test scores of the corresponding cohort who took the student tests as 15-year olds. Such matching is reasonable given that both sets of tests capture similar skills: student test scores cover reading, maths and science; PIAAC adult test scores cover literacy, numeracy and problem solving. The three components of both student and adult test scores are highly correlated with one another. Hence, an average of the three components can be conveniently used as a summary measure for student and adult test scores, respectively….”

Matching PIAAC and PISA scores by cohort

Steve Comment: “…Read the underlying OECD report the blog post was based on, in terms of your question about what they meant by“…PIAAC test scores are regressed on matched PISA test scores and mean years of schooling. The estimation results show that the elasticity of the stock of human capital with respect to the quality of education is three to four times larger than for the quantity of education…”in their blog post (Note here STS is their “Transformed Student Test Score Series”)To capture quantity aspects, mean years of schooling (MYS) are added, on top of the student test scores,as an explanatory variable to the adult test score regressionswhere…. is mean years of schooling, all indexed by cohort c and country i, ensuring that the MYS data matches the same cohort for which the adult test scores are available. An increase in the years spent in the education system boost adult test scores in a statistically significant manner (Table 4, equations and ),although contrary to student test scores, the positive effect of mean years of schooling on adult test scores does not decline for older cohorts. The elasticity of cohort-specific adult skills with respect to student test scores is three to four times higher than the elasticity with respect to mean years of schooling.This result can be contrasted with the measures of learning adjusted years of schooling, which have been used in the literature but by construction usually impose the same elasticity for the quantity and quality components. Furthermore, this result can be used to combine the quality and quantity measures of education in a single stock measure of human capital…”…”