Edward Conard

Top Ten New York Times Bestselling Author

  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Friday, May 3, 2019

AI Summary. Analysis of a long-running U.S. youth survey finds that once selection into college is accounted for, intergenerational income mobility among college graduates is similar to that of non-graduates. The authors argue the apparent mobility advantage of a degree largely reflects selection on attributes such as ability and motivation rather than an equalizing effect of college.

Xiang Zhou American Sociological Review
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Research by @XiangZhou suggests that higher education’s impact on mobility is driven by selection effects, not equalization. Intergenerational income mobility among college graduates is similar to non-graduates once adjusted for selection processes.

Does a college degree raise income mobility or just reflect selection?

Core argument: Analysis of the National Longitudinal Survey of Youth 1979 finds that once selection is accounted for, intergenerational income mobility among college graduates is similar to that of non-graduates.

Analysis of the National Longitudinal Survey of Youth 1979 reveals that intergenerational income mobility among college graduates, once adjusted for selection processes, is similar to that of non-graduates. This suggests that merely increasing the number of college graduates may not enhance mobility in the U.S. The study highlights that high mobility among degree holders often reflects selectivity based on attributes like ability and motivation, particularly from low and moderate-income families. While conditional mobility is higher for graduates, controlled mobility shows little difference between graduates and non-graduates. Selective colleges show a large but statistically insignificant equalizing effect, whereas nonselective colleges do not. The findings indicate that the perceived "college premium" in mobility is largely due to selection rather than equalization, with family income strongly correlating with characteristics that perpetuate inequality, such as race, cognitive ability, and educational expectations.

Takeaways by Macro Roundup® AI

  1. Analysis of the National Longitudinal Survey of Youth 1979 finds that once selection is accounted for, intergenerational income mobility among college graduates is similar to that of non-graduates.
  2. Conditional mobility is higher for college graduates, but controlled mobility differs little between graduates and non-graduates, suggesting the mobility "college premium" largely reflects selection on attributes such as ability and motivation rather than equalization.
  3. Selective colleges show a large but statistically insignificant equalizing effect while nonselective colleges show none, and family income correlates strongly with race, cognitive ability, and educational expectations, indicating that increasing the number of graduates alone may not enhance U.S. mobility.

higher education's impact on mobility is driven by selection effects,

"....estimate the degree of intergenerational income mobility among college graduates purged of selection processes that may drive up observed mobility in this subpopulation. Analyzing data from the National Longitudinal Survey of Youth 1979,I find that once selection processes are adjusted for, intergenerational income mobility among college graduates is very close to that among non-graduates.This finding suggests that expanding the pool of college graduates per se is unlikely to boost intergenerational income mobility in the United States..... In fact, to the extent that college graduates from low and moderate-income families are more selected on such individual attributes as ability and motivation than those from high-income families, the high mobility observed among bachelor’s degree holders may simply reflect varying degrees of selectivity of college graduates from different family backgrounds. This study represents my attempt to distinguish the equalization and selection effects of college. First, I formalized the selection process using the language of DAGs, and defined two measures of intergenerational mobility—conditional mobility and controlled mobility—for college graduates. While conditional mobility reflects what we observe among actual college graduates, controlled mobility reflects what we would observe if, given parental income, college graduation did not depend on other predictors of adult family income. By fixing the associations between parental income and a range of pre-college individual characteristics at their baseline levels in the general population, controlled mobility disentangles the causal effect of college from selection processes, thus directly informing the degree to which we can promote mobility by expanding the pool of college graduates. Then, I employed a novel reweighting method, called “residual balancing,” to estimate controlled mobility empirically. Using the NLSY79 data, I examined the equalization effects of both college completion in general and selective/nonselective colleges in particular. Results show that while conditional mobility is substantially higher among college graduates than non-graduates, controlled mobility differs little between the two groups. When we break down college graduates into those who attended selective institutions and those who did not, we find little evidence for an equalizing effect of nonselective colleges. Selective colleges, on the other hand, exhibit a substantively large, yet statistically insignificant, equalizing effect.Overall, our findings suggest that the “college premium” in intergenerational income mobility is largely driven by selection processes rather than an equalizing effect of a baccalaureate degree.....As shown in Figure 3, family income correlates strongly not only with other ascribed characteristics such as race, ethnicity, mother’s education, and family structure, but also with achieved characteristics such as cognitive ability and educational expectations. It is the combination of these origin-based differences that perpetuate and reproduce inequality—by shaping the type of postsecondary institutions attended, the degree of engagement in the college extracurriculum, the level of parental economic support after graduation, and patterns of marital formation and partner choice. As a result, the overall influence of parental income stays unabated among college graduates, despite the appearance of high (conditional) mobility in this subpopulation...."
Xiang Zhou, "Equalization or Selection? Reassessing the “Meritocratic Power” of a College Degree in Intergenerational Income Mobility,"American Sociological Review, April 30, 2019, https://journals.sagepub.com/doi/abs/10.1177/0003122419844992

Monday, May 6, 2019

AI Summary. A tightened U.S. labor market with unemployment at 3.6%, the lowest in nearly 50 years, has lifted wages 3.2% over the past year, outpacing inflation. Labor demand continues to exceed supply in sectors such as construction and healthcare, prompting employers to offer higher pay and better benefits.

Ben Casselman The New York Times
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A decade after the recession, the US labor market has tightened, leading to notable wage growth. Unemployment rates have fallen to 3.6%, driving wages up by 3.2% over the past year, outpacing inflation.

Does a tighter labor market explain faster wage growth?

Core argument: U.S. wages rose 3.2% over the past year, outpacing inflation and delivering real income gains, as unemployment fell to 3.6%, the lowest in nearly 50 years.

A decade after the recession, the U.S. labor market has tightened significantly, leading to notable wage growth. Unemployment rates have fallen to 3.6%, the lowest in nearly 50 years, creating a competitive environment for employers seeking skilled workers. This scarcity of labor has driven wages up by 3.2% over the past year, outpacing inflation and providing real income gains for workers. The labor force participation rate [LFP] has also seen a modest increase, indicating more individuals are entering the workforce. However, the demand for labor continues to exceed supply, particularly in sectors like construction and healthcare, where job openings have surged by 20% since 2018. This imbalance is prompting companies to offer higher salaries and better benefits to attract and retain talent, marking a significant shift from the stagnant wage growth observed in the years immediately following the recession.

Takeaways by Macro Roundup® AI

  1. U.S. wages rose 3.2% over the past year, outpacing inflation and delivering real income gains, as unemployment fell to 3.6%, the lowest in nearly 50 years.
  2. Job openings in sectors including construction and healthcare have risen 20% since 2018, with labor demand exceeding supply and prompting employers to offer higher salaries and better benefits.
  3. The U.S. labor force participation rate has increased modestly, indicating more individuals are entering the workforce.

Ben Casselman, "Why Wages Are Finally Rising, 10 Years After the Recession,"The New York Times, May 2, 2019, https://www.nytimes.com/2019/05/02/business/economy/wage-growth-economy.html

AI Summary. The global pool of advanced analytics workers is projected to double to 1m from 500,000, with India tripling its candidates for such roles to over 200,000 from 65,000. The US is projected to face shortages of data engineers and architects alongside a potential oversupply of data scientists.

Chris Brahm Bain & Company
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The global demand for data analytics workers is set to rise significantly, with the talent pool expected to double by 2020, reaching 1m people from 500,000 in 2018.

Is the growing analytics talent pool matching where skills are needed?

Core argument: The global pool of advanced analytics talent is projected to double from 500,000 in 2018 to 1m by 2020, amid rising demand for data analytics workers.

The global demand for data analytics workers is set to rise significantly, with the talent pool expected to double by 2020, reaching 1m people from 500,000 in 2018. India is leading this growth, tripling its candidates for advanced analytics roles from 65,000 in 2018 to over 200,000 by 2020. Despite this increase, the US will face shortages in critical roles like data engineers and architects, while potentially having an oversupply of data scientists. Companies are advised to adopt a tiered talent strategy, combining new hires, internal retraining, and leveraging external resources such as offshore data hubs and third-party firms. This approach helps address talent bottlenecks and supports the growing analytic intensity across sectors like retail, media, and technology, where advanced analytics roles are increasingly vital.

Takeaways by Macro Roundup® AI

  1. The global pool of advanced analytics talent is projected to double from 500,000 in 2018 to 1m by 2020, amid rising demand for data analytics workers.
  2. India is projected to triple its candidates for advanced analytics roles from 65,000 in 2018 to over 200,000 by 2020, leading global growth in the talent pool.
  3. The US is projected to face shortages of data engineers and architects alongside a potential oversupply of data scientists, and companies are advised to combine new hires, internal retraining, offshore data hubs, and third-party firms.

Chris Brahm, Arpan Sheth, Velu Sinha and Jessica Dai, "Solving the New Equation for Advanced Analytics Talent," Bain & Company, April 30, 2019, https://www.bain.com/insights/solving-the-new-equation-for-advanced-analytics-talent/

Solving the New Equation for Advanced Analytics Talent

At a Glance

In the next two years, the global supply of advanced analytics talent will double, with India’s talent pool growing especially quickly.
In the US, talent shortages will persist for experienced hires and certain key jobs, like data engineers and architects.
Companies will struggle to hire experts away from the analytically mature sectors that are the most attractive to employees and that themselves plan to expand quickly.
A successful tiered talent strategy incorporates new hires and internal retraining, while also tapping data hubs, third-party service firms and crowdsourcing.

Innovation in analytics is a priority for almost every organization today. Yet bottlenecks in the key areas of talent, data and analytics strategy often block progress. In order to better understand the talent bottleneck and how to release it, Bain & Company assessed the global market for analytics talent—both supply and demand—interviewing practitioners and experts, reviewing job boards and professional community listings, and studying educational program statistics.

We found a workforce on the verge of dramatic change.

For years, companies have been stuck chasing either a limited supply of experienced professionals or new university graduates. Today, however, the global supply of advanced analytics talent is about to explode. By 2020, the advanced analytics talent pool is expected to reach 1 million people, up from half a million in 2018.

The growth in advanced analytics talent arises from a pivot in traditional and nontraditional education so rapid that it’s hard to find a historical precedent. The best comparison may be the 1990s, when India invested heavily in training programmers, especially in legacy systems, ahead of Y2K systems upgrades. That was when the subcontinent became the outsourcing giant it remains today.

Once again, India is taking the lead, but this time through a combination of retraining and a flood of graduates with advanced degrees in subjects suited to advanced analytics work, like computer science and data science. By the end of 2020, Bain estimates that India will have three times as many candidates for advanced analytics work as it did in 2018, expanding from 65,000 people to more than 200,000 in just two years. The analytics talent supply will climb in China, Western Europe and North America as well, but not quite as quickly.

This ramp-up, however, won’t entirely solve the advanced analytics talent challenge. Many countries will still have fewer analytics experts than needed. Most available advanced analytics talent has recently graduated, but people with greater tenure, who are needed to lead and manage these teams, are in short supply. The United States will suffer shortages in certain critical roles, like data engineers and architects, while facing a possible oversupply of data scientists.

Additional long-term demand will come from sectors like retail, media, consumer goods, and industrial goods and services, as companies apply analytics capabilities to their businesses. According to Bain’s research, among banks today, slightly more than 1% of white-collar employees work in advanced analytics, on average. By contrast, technology companies average nearly 3% of staff aligned with advanced analytics, and the most analytically advanced digital native companies have 10% (see Figure 1). The business models differ greatly across these sectors, of course, but it’s clear that, in nearly every large enterprise, the analytic intensity of the employee base is increasing.

Solving the New Equation for Advanced Analytics Talent: Extended Excerpt Image 1


Building an advanced analytics team

Companies should not expect to fill this gap entirely by wooing experienced talent from other employers. The most analytically mature sectors plan to expand their teams fastest, and employees are most interested in working for companies with well-established track records in analytics, our recent survey of more than 200 industry participants found.

Creative, flexible approaches for expanding the talent base include building centers of excellence for pools of hired analytics experts, and also retraining capable existing employees and giving them access to automation tools. Importantly, rather than trying to do everything in-house, a tiered talent strategy should focus a core, in-house analytics team on strategic tasks while tapping offshore data hubs, third-party service firms and crowdsourcing for other work. Even the most sophisticated companies leverage a combination of internal and external supply chains for analytics capabilities.

What is the optimal blend of advanced analytics roles? How are teams best configured? The exact balance varies depending on the sector and maturity of a company’s analytics practice, but teams will draw from eight key roles (see Figure 2). With companies hiring to create balanced advanced analytics teams, certain skills are in higher demand.

Solving the New Equation for Advanced Analytics Talent: Extended Excerpt Image 2


A mixed global outlook

The growth in advanced analytics-trained talent will occur primarily outside the US (see Figure 3). Long the largest market for analytics talent, the US will retain that top spot, but other global tech powers are catching up.

Solving the New Equation for Advanced Analytics Talent: Extended Excerpt Image 3


The outlook is especially bright in India, where two trends are simultaneously expanding the talent pool. First, STEM undergraduate and graduate degree holders, whose programs of study emphasize data and analytics skills, continue to join the workforce in increasing numbers. Augmenting that is India’s deep existing ecosystem in information technology, especially in programming and systems integration. Outsourcing firms and the India IT centers of global corporations house many ideal candidates for learning new advanced analytics skills. These two sources have combined to make India a vital hub of analytics expertise and have fueled the growth of its analytics outsourcing industry.

The future is less clear for China. Thanks to its relatively supportive regulatory environment and access to consumer data, China is often described as winning the race to dominate artificial intelligence and advanced analytics. But on advanced analytics talent, the country may need to accelerate. Recent trends point to a growing focus on talent in China, including a significant expansion in data science bachelor's degree programs and high levels of US recruiting and pay among Chinese digital natives and technology companies. Without an expansion in supply, talent could become a bottleneck, slowing analytics progress in China, especially for companies in traditional sectors.

Breaking the talent bottleneck

The historical shortage of analytics talent has caused many organizations to rely on a combination of internal and external advanced analytics expertise. This hybrid model also turns out to be a good match for the breadth of advanced analytics expertise needed in the future. A multilayered approach will continue to make more sense than full vertical integration for many companies. When possible, companies should develop critical mass internally in the most important aspects of advanced analytics, such as data-science team leadership and model development, and tap the external supply chain for less-critical skills, like tactical data management and model maintenance. Today, only 30% of companies are fully integrated in advanced analytics. The other 70% augment their internal skills with some combination of offshore outsourcing, freelancers, advanced analytics consultants and crowdsourcing.

Part of developing this talent ecosystem involves harnessing shadow analytics talent—that is, taking people currently walking the halls and helping them develop new analytical skills. Existing employees already know the company, the industry and how to operate effectively across the organization. Many have the quantitative background to learn analytical skills, and nearly a quarter of Bain survey respondents report that their companies have implemented advanced analytics training programs.

IBM’s Data Scientist Academy, for example, is an eight-day boot camp followed by individual online learning, and includes shadowing IBM data scientists and a capstone project. Airbnb’s Data University offers more than 30 classes in data awareness, collection, visualization and data at scale, and more than 500 employees (1 out of every 8) took part during the university’s first six months. Other training options include free MOOCs—massive open online courses—and paid retraining programs.

Some companies with a good data science workbench and the right set of data engineering tools also use advanced analytics automation to enable people without strong coding skills to build models and engineer data. Of survey respondents currently using technologies to automate advanced analytics tasks, more than half say these technologies enable existing staff to be rapidly trained to take on responsibilities of a data scientist or data engineer.

Fostering the talent ecosystem

Embracing this tiered approach will help companies today and in the future. It allows them to tap into the rapidly growing global talent supply and to create a more flexible model, all while leaving room to redeploy existing talent in new and creative ways. Companies cannot afford to let a bottleneck in analytics talent slow them down, and they don’t have to.

Chris Brahm is a Bain & Company partner in San Francisco and leads the firm’s Global Advanced Analytics practice. Arpan Sheth is a Bain partner based in the Bangalore office and leads the firm’s Information Technology practice in Asia-Pacific. Velu Sinha is a Bain partner in Shanghai, and Jessica Dai is a principal based in San Francisco.