Systems Thinking In Entrepreneurship Or: How I Learned To Stop Worrying And Love “Entrepreneurial Ecosystems”

As someone who works remotely and travels quite a long way to be with my colleagues, I really value my “water cooler moments” in the FAS office, when I have them. The idea for this series came from one such moment, when Josh Schoop and I were sharing a sparkling water break. Systems thinking, we realized, is a through line in many parts of our work, and part of the mental model that we share that leads to effective change making in complex, adaptive systems. In the geekiest possible terms:

A diagram of 'water cooler conversations' from a Systems Thinking perspective
Figure 1: Why Water Cooler Conversations Work

Systems analysis had been a feature of Josh’s dissertation, while I had had an opportunity to study a slightly more “quant” version of the same concepts under John Sterman at MIT Sloan, through my System Dynamics coursework. The more we thought about it, systems thinking and system dynamics were present across the team at FAS–from our brilliant colleague Alice Wu, who had recently given a presentation on Tipping Points, to folks who had studied the topic more formally as engineers, or as students at Michigan and MIT.  This led to the first meeting of our FAS “Systems Thinking Caucus” and inspired  a series of blog posts which intend to make this philosophical through-line more clear. This is just the first, and describes how and why systems thinking is so important in the context of entrepreneurship policy, and how systems modeling can help us better understand which policies are effective. 

The first time I heard someone described as an “ecosystem builder,” I am pretty sure that my eyes rolled involuntarily. The entrepreneurial community, which I have spent my career supporting, building, and growing, has been my professional home for the last 15 years. I came to this work not out of academia, but out of experience as an entrepreneur and leader of entrepreneur support programs. As a result, I’ve always taken a pragmatic approach to my work, and avoided (even derided) buzzwords that make it harder to communicate about our priorities and goals. In the world of tech startups, in which so much of my work has roots, buzzwords from “MVP” to “traction” are almost a compulsion. Calling a community an “ecosystem” seemed no different to me, and totally unnecessary. 

And yet, over the years, I’ve come to tolerate, understand, and eventually embrace “ecosystems.” Not because it comes naturally, and not because it’s the easiest word to understand, but because it’s the most accurate descriptor of my experience and the dynamics I’ve witnessed first-hand. 

So what, exactly, are innovation ecosystems? 

My understanding of innovation ecosystems is grounded first in the experience of navigating one in my hometown of Kansas City–first, as a newly minted entrepreneur, desperately seeking help understanding how to do taxes, and later as a leader of an entrepreneur support organization (ESO), a philanthropic funder, and most recently, as an angel investor. It’s also informed by the academic work of Dr. Fiona Murray and Dr. Phil Budden. The first time that I saw their stakeholder model of innovation ecosystems, it crystallized what I had learned through 15 years of trial-and-error into a simple framework. It resonated fully with what I had seen firsthand as an entrepreneur desperate for help and advice–that innovation ecosystems are fundamentally made up of people and institutions that generally fall into the same categories:  entrepreneurs, risk capital, universities, government, or corporations. 

Over time–both as a student and as an ecosystem builder, I came to see the complexity embedded in this seemingly simple idea and evolved my view. Today, I amend that model of innovation ecosystems to, essentially, split universities into two stakeholder groups: research institutions and workforce development. I take this view because, though not every secondary institution is a world-leading research university like MIT, smaller and less research-focused colleges and universities play important roles in an innovation ecosystem. Where is the room for institutions like community colleges, workforce development boards, or even libraries in a discussion that is dominated by the need to commercialize federally-funded research? Two goals–the production of human capital and the production of intellectual property–can also sometimes be in tension in larger universities, and thus are usually represented by different people with different ambitions and incentives. The concerns of  a tech transfer office leader are very different from those of a professor in an engineering or business school, though they work for the same institution and may share the same overarching aspirations for a community. Splitting the university stakeholder into two different stakeholder groups makes the most sense to me–but the rest of the stakeholder model comes directly from Dr. Murray and Dr. Budden. 

IMAGE: An innovation ecosystem stakeholder model a network of labeled nodes, including entrepreneur, workforce, research, corporations, government, and capital nodes, each connected to the other.
Figure 2: Innovation Ecosystem Stakeholder Model

One important consideration in thinking about innovation ecosystems is that boundaries really do matter. Innovation ecosystems are characterized by the cooperation and coordination of these stakeholder groups–but not everything these stakeholders do is germane to their participation in the ecosystem, even when it’s relevant to the industry that the group is trying to build or support. 

As an example, imagine a community that is working to build a biotech innovation ecosystem. Does the relocation of a new biotech company to the area meaningfully improve the ecosystem? Well, that depends! It might, if that company actively engages in efforts to build the ecosystem say, by directing an executive to serve on the board of an ecosystem building nonprofit, helping to inform workforce development programs relevant to their talent needs, instructing their internal VC to attend the local accelerator’s demo day, offering dormant lab space in their core facility to a cash-strapped startup at cost, or engaging in sponsored research with the local university. Relocation of the company may not improve the ecosystem  if they simply happen to be working in the targeted industry and receive a relocation tax credit. In short, by itself, shared work between two stakeholders on an industry theme does not constitute ecosystem building. That shared work must advance a vision that is shared by all of the stakeholders that are core to the work.

Who are the stakeholders in innovation ecosystems? 

Innovation ecosystems are fundamentally made up of six different kinds of stakeholders, who, ideally, work together to advance a shared vision  grounded in a desire to make the entrepreneurial experience easier. One of the mistakes I often see in efforts to build innovation ecosystems is an imbalance or an absence of a critical stakeholder group. Building innovation ecosystems is not just about involving many people (though it helps), it’s about involving people that represent different institutions and can help influence those institutions to deploy resources in support of a common effort. Ensuring stakeholder engagement is not a passive box-checking activity, but an active resource-gathering one. 

An innovation ecosystem in which one or more stakeholders is absent will likely struggle to make an impact. Entrepreneurs with no access to capital don’t go very far, nor do economic development efforts without government buy-in, or a workforce training program without employers. 

In the context of today’s bevvy of federal innovation grant opportunities with 60-day deadlines, it can be tempting to “go to war with the army you have” instead of prioritizing efforts to build relationships with new corporate partners or VCs. But how would you feel if you were “invited” to do a lot of work and deploy your limited resources to advance a plan that you had no hand in developing? Ecosystem efforts that invest time in building relationships and trust early will benefit from their coordination, regardless of federal funding.  

These six stakeholder groups are listed in Figure 2 and include: 

In the context of regional, place-based innovation clusters (including tech hubs), this stakeholder model is a tool that can help a burgeoning coalition both assess the quality and capacity of their ecosystem in relation to a specific technology area and provide a guide to prompt broad convening activities. From the standpoint of a government funder of innovation ecosystems, this model can be used as a foundation for conducting due diligence on the breadth and engagement of emerging coalitions. It can also be used to help articulate the shortcomings of a given community’s engagements, to highlight ecosystem strengths and weaknesses, and to design support and communities of practice that convene stakeholder groups across communities.

What about entrepreneur support organizations (ESO)? What about philanthropy? Where do they fit into the model? 

When I introduce this model to other ecosystem builders, one of the most common questions I get is, “where do ESOs fit in?” Most ESOs like to think of themselves as aligned with entrepreneurs, but that merits a few cautionary notes. First, the critical question you should ask to figure out where an ESO, a Chamber or any other shape-shifting organization fits into this model is, “what is their incentive structure?” That is to say, the most important thing is to understand to whom an organization is accountable. When I worked for the Enterprise Center in Johnson County, despite the fact that I would have sworn up-and-down that I belonged in the “E” category with the entrepreneurs I served, our sustaining funding was provided by the county government. My core incentive was to protect the interests of a political subdivision of the metro area, and a perceived failure to do that would have likely resulted in our organization’s funding being cut (or at least, in my being fired from it). That means that I truly was a “G,” or a government stakeholder. So, intrepid ESO leader, unless the people that fund, hire, and fire you are majority entrepreneurs, you’re likely not an “E.”

The second danger of assuming that ESOs are, in fact, entrepreneurs, is that it often leads to a lack of actual entrepreneurs in the conversation. ESOs stand in for entrepreneurs who are too busy to make it to the meeting. But the reality is that even the most well-meaning ESOs have a different incentive structure than entrepreneurs–meaning that it is very difficult for them to naturally represent the same views. Take for instance, a community survey of entrepreneurs that finds that entrepreneurs see “access to capital” as the primary barrier to their growth in a given community. In my experience, ESOs generally take that somewhat literally, and begin efforts to raise investment funds. Entrepreneurs, on the other hand who simply meant “I need more money,” might see many pathways to getting it, including by landing a big customer. (After all, revenue is the cheapest form of cash.) This often leads ESOs to prioritize problems that match their closest capabilities, or the initiatives most likely to be funded by government or philanthropic grants. Having entrepreneurs at the table directly is critically important, because they see the hairiest and most difficult problems first–and those are precisely the problems it take a big group of stakeholders to solve. 

Finally, I have seen folks ask a number of times where philanthropy fits into the model. The reality is that I’m not sure. My initial reaction is that most philanthropic organizations have a very clear strategic reason for funding work happening in ecosystems–their theory of change should make it clear which stakeholder views they represent. For example, a community foundation might act like a “government” stakeholder, while a funder of anti-poverty work who sees workforce development as part or their theory of change is quite clearly part of the “W” group. But not every philanthropy has such a clear view, and in some cases, I think philanthropic funders, especially those in small communities, can think of themselves as a “shadow stakeholder,” standing in for different viewpoints that are missing in a conversation. Finally, philanthropy might play a critical and underappreciated role as a “platform creator.” That is, they might seed the conversation about innovation ecosystems in a community, convene stakeholders for the first time, or fund activities that enable stakeholders to work and learn together, such as planning retreats, learning journeys, or simply buying the coffee or providing the conference room for a recurring meeting. Finally, and especially right now, philanthropy has an opportunity to act as an “accelerant,” supporting communities by offering the matching funds that are so critical to their success in leveraging federal funds.  

Why is “ecosystem” the right word? 

Innovation ecosystems, like natural systems, are both complex and adaptive. They are complex because they are systems of systems. Each stakeholder in an innovation ecosystem is not just one person, but a system of people and institutions with goals, histories, cultures, and personalities. Not surprisingly, these systems of systems are adaptive, because they are highly connected and thus produce unpredictable, ungovernable performance. It is very, very difficult to predict what will happen in a complex system, and most experts in fields like system dynamics will tell you that a model is never truly finished, it is just “bounded.” In fact, the way that the quality of a systems model is usually judged is based on how closely it maps to a reference mode of output in the past. This means that the best way to tell whether your systems model is any good is to give it “past” inputs, run it, and see how closely it compares to what actually happened. If I believe that job creation is dependent on inflation, the unemployment rate, availability of venture capital, and the number of computer science majors graduating from a local university, one way to test if that is truly the case is to input those numbers over the past 20 years, run a simulation of how many jobs would be created, according to the equations in my model, and seeing how closely that maps to the actual number of jobs created in my community over the same time period. If the line maps closely, you’ve got a good model. If it’s very different, try again, with more or different variables. It’s quite easy to see how this trial-and-error based process can end up with an infinitely expanding equation of increasing complexity, which is why the “bounds” of the model are important. 

Finally, complex, adaptive systems are, as my friend and George Mason University Professor Dr. Phil Auerswald says, “self-organizing and robust to intervention”. That is to say, it is nearly impossible to predict a linear outcome (or whether there will be any outcome at all) based on just a couple of variables. This means that the simple equation(money in = jobs out) is wrong. To be better able to understand the impact of a complex, adaptive system requires mapping the whole system and understanding how many different variables change cyclically and in relation to each other over a long period of time. It also requires understanding the stochastic nature of each variable. That is a very math-y way of saying it requires understanding the precise way in which each variable is unpredictable, or the shape of its bell-curve.

All of this is to say that understanding and evaluation of innovation ecosystems requires an entirely different approach than the linear jobs created = companies started * Moretti multiplier assumptions of the past. 

So how do you know if ecosystems are growing or succeeding if the number of jobs created doesn’t matter? 

The point of injecting complexity thinking into our view of ecosystems is not to create a sense of hopelessness. Complex things can be understood–they are not inherently chaotic. But trying to understand these ecosystems through traditional outputs and outcomes is not the right approach since those outputs and outcomes are so unpredictable in the context of a complex system. We need to think differently about what and how we measure to demonstrate success. The simplest and most reliable thing to measure in this situation then becomes the capacities of the stakeholders themselves, and the richness or quality of the connections between them. This is a topic we’ll dive into further in future posts.

ALI Releases Statement on the President’s FY2024

WASHINGTON, D.C. — The Alliance for Learning Innovation (ALI) applauds the increases proposed for education research and development (R&D) and innovation in the President’s budget request. These include the $870.9 million proposed for the Institute of Education Sciences (IES), including $75 million for a National Center for Advanced Development in Education (NCADE), the $405 million proposed for the Education Innovation and Research (EIR) program and the $1.4 billion for the National Science Foundation’s (NSF) Directorate for STEM Education. These investments represent real commitments to advancing an inclusive education research system that centers students, teachers, and communities.

These recommendations build upon the bipartisan interest in utilizing education R&D to  accelerate learning recovery, increase student achievement, and ensure students and teachers are prepared for the continued impact technology will have on teaching and learning. National and economic security depends on the success of our students and ALI appreciates the priorities this budget request places on fostering innovations in education that will support U.S. competitiveness.

Dan Correa, CEO of the Federation of American Scientists and co-lead of ALI notes, “Investments in education research and development hold so much promise for dramatically improving gaps in student achievement. Learning recovery, workforce development, and global competition all demand a pool of talent that can only come from an education system that meets the needs of diverse learners. The President’s budget request recognizes that more robust education R&D is needed to support bold innovations that meet the needs of students, teachers, families, and communities.”

This budget will allow IES and other federal agencies the ability to build on boundary-pushing efforts like the National AI Institute for Exceptional Education, which is supporting advancements in AI, human-AI interaction, and learning science to improve educational outcomes for children with speech and language related challenges.

For too long, federal support for education R&D has languished while resources and attention have been devoted to R&D in health care, defense, energy, and other fields. Today’s budget represents a critical step forward in addressing this deficiency. The Alliance for Learning Innovation looks forward to championing the continued development of an education R&D ecosystem that will lead to the types of groundbreaking developments and advancements we see in health care and defense; thus affording students everywhere access to fulfilling futures.

For more information about the Alliance for Learning Innovation, please visit https://www.alicoalition.org/.

Safeguarding Benchtop DNA Synthesis

Benchtop DNA synthesizers could become more ubiquitous, and it’s up to policymakers to chart the way forward

The genetic blueprints for humans, plants, disease-causing bacteria, and all other living things are written in DNA, and machines capable of synthesizing DNA are becoming more accessible to potential users. Benchtop DNA synthesizers promise to increase the speed and efficiency of research in academic and industrial laboratories; however, it will be critical to incorporate safeguards into benchtop machines to prevent the printing of DNA sequences that would be used for harmful purposes. Researchers should be permitted to operate a benchtop DNA synthesizer to, for instance, make genetic material that is then used by a microbe to build a biofuel. But, aside from research conducted by pre-approved specialists, printing DNA that codes for deadly agents like the ricin or diphtheria protein toxins, for example, should be prohibited. As instruments capable of small-scale, rapid-turnaround DNA synthesis are already starting to enter the market, policymakers may be faced with a new era of democratized DNA synthesis, and should grapple with how to maximize the benefits of this technology while minimizing potential harm.

A National Academies of Sciences, Engineering, and Medicine report speculated that by 2027, individuals both with and without formal scientific training would be rapidly prototyping and developing biological designs and products. In both institutional and DIY contexts, there are protections that could be put in place to drastically reduce the likelihood of the misuse of benchtop DNA synthesizers. For instance, a January 2020 report from the World Economic Forum, crafted in collaboration with the Nuclear Threat Initiative, recommends that benchtop DNA synthesizers:

Before efficient benchtop DNA synthesizers become even more ubiquitous, decision-makers have an opportunity to craft forward-thinking policies that both (i) protect the technology from misuse and (ii) promote its potential to advance human health, a cleaner environment, and many other public goods.

This CSPI Science and Technology Policy Snapshot expands upon a scientific exchange between Congressman Bill Foster (D, IL-11) and his new FAS-organized Science Council.

A health-oriented ARPA could help the U.S. address challenges like antimicrobial resistance

To help catalyze innovation in the health and biomedical sciences, research and development (R&D) paradigms with a track record of producing ‘moonshot’-scale breakthroughs – such as the Advanced Research Projects Agency (ARPA) model – stand at the ready. The Biden Administration has recognized this, proposing the establishment of an ARPA for health (ARPA-H) as part of its fiscal year 2022 budget request. Done right, ARPA-H would be created in the image of existing ARPAs – DARPA (defense), ARPA-E (energy), and IARPA (intelligence) – and be capable of mobilizing federal, state, local, private sector, academic, and nonprofit resources to directly address the country’s most urgent health challenges, such as the high cost of therapies for diseases like cancer, or antimicrobial resistance. During a recent House Energy and Commerce Committee hearing, Chairwoman Anna Eshoo (D-CA) raised the Administration’s proposal for ARPA-H with Department of Health and Human Services (HHS) Secretary Xavier Becerra, expressing her interest in exploring how to best position a potential ARPA-H for success.

Keys to the ARPA model

The success of the ARPA model is attributed in part to the high level of autonomy with which its program leaders select R&D projects (compared to those at traditional federal research agencies), a strong sense of agency mission, and a culture of risk-taking with a tolerance for failure, resulting in a great degree of flexibility to pursue bold agendas and adapt to urgent needs. Policymakers have debated situating a potential ARPA-H within the National Institutes of Health (NIH), or outside of NIH, elsewhere under the umbrella of HHS. Regardless, it is essential that ARPA-H retain an independent and innovative culture.

The first ARPA – DARPA – was established in 1958, the year after Sputnik was launched, and is credited with developing GPS, the stealth fighter, and computer networking. DARPA continues to serve its customer – the Department of Defense – by developing groundbreaking defense technologies and data analysis techniques. Nevertheless, DARPA operates separately from its parent organization. This is also true of ARPA-E, which was launched in 2007 based on a recommendation from a National Academies consensus study report which called for implementing the DARPA model to drive “transformational research that could lead to new ways of fueling the nation and its economy,” and IARPA, created in 2006, to foster advances in intelligence collection, research, and analysis.

If ARPA-H is organized within NIH, it is essential that it maintain the innovative spirit and independence characteristic of established ARPAs. NIH already has some experience overseeing a partially independent entity: the National Cancer Institute (NCI). Compared to other NIH institutes, NCI’s unique authorities include:

This level of independence has contributed to NCI achieving a number of significant milestones in cancer treatment, including developing a chemotherapy treatment to cure choriocarcinoma (a rare type of cancer that starts in the womb), publishing the now-widely-used Breast Cancer Risk Assessment Model, and creating an anticancer drug for ovarian cancer that was unresponsive to other treatments.

If the NCI model were to be used as the foundation for the launch of ARPA-H, insulation from political considerations, whether those of Congress or the Executive Branch, would be critical. With DARPA-like autonomy, a potential ARPA-H could help push the boundaries of enrichments to human health.

Antimicrobial resistance as a case study for an ARPA-H

An example of a grand challenge that an ARPA-H could take on is addressing antimicrobial resistance, a worsening situation that, without intervention, will lead to a significant public health crisis. Antimicrobial resistance occurs when “bacteria, viruses, fungi, and parasites change over time and no longer respond to medicines, making infections harder to treat and increasing the risk of disease spread, severe illness, and death.” Microbes have the potential to gain resistance to drugs when not all of the pathogens or parasites are killed by a treatment, either because the treatment was the not correct option for the illness (like using antibiotics for viruses), or refraining from completing a prescribed course of an antimicrobial drug. The organisms that are not killed, presumably because they harbor genetic factors that confer resistance, then reproduce and pass along those genes, which make it harder for the treatments to kill them.

The most immediate concerns regarding antimicrobial resistance come from bacteria and fungi. The CDC considers some of the biggest threats to be Acinetobacter, Candida auris, and C. difficile, which are often present in healthcare and hospital settings and mainly threaten the lives of those with already weakened immune systems. Every year in the U.S., almost 3 million people are infected with antimicrobial-resistant bacteria or fungi, and as a result, more than 35,000 people die. While the toll of antibiotic resistance in the U.S. is devastating, the global outlook is perhaps even more concerning: in 2019, the United Nations warned that if no action is taken, antimicrobial resistance could cause 10 million deaths per year worldwide by 2050.

Developing new and effective antibiotics can help counter antimicrobial resistance; however, progress has been extremely slow. The last completely new class of antibiotics was discovered in the late 1980s, and developing new antibiotics is often not profitable for pharmaceutical companies. It is estimated that it takes $1.5 billion to create a new antibiotic, while the average revenue is about $46 million per year. In addition, while pharmaceutical companies receive an exclusivity period during which competitors cannot manufacture a generic version of their drug, the period is only five to ten years, which is too short to recoup the cost of research and development. Furthermore, doctors are often hesitant to prescribe new antibiotics in hopes of delaying the development of newly drug-resistant microbes, which also contributes to driving down the amount pharmaceutical companies earn for antibiotics.

Early last year, the World Health Organization reported that out of 60 antibiotics in development, there would be very little additional benefit over existing treatments, and few targeted the most resistant bacteria. Moreover, the ones that appeared promising will take years to get to the market. This year, Pew Research conducted a study on the current antibiotic development landscape and found that out of 43 antibiotics under development, at least 19 have the potential to treat the most resistant bacteria. However, the likelihood of all, or even some of these products making it to patients is low: over 95 percent of the products in development are being studied by small companies, and more than 70 percent of these companies do not have any other products on the market.

There is both a dire need for new innovations in the space, such as using cocktails of different viruses that attack bacteria to treat infections, and a gap between the research into and commercialization of new antibiotics – a perfect opportunity for a potential ARPA-H to make an impact. With this new agency, experimental treatments could be supported through the technology transfer process and matured to the point that the private sector is able to take the baton and move a new antimicrobial to market. This would be revolutionary for public health, and, combined with improved messaging around best practices for the use of antibiotics, save many lives.

Moving forward

The need for, structure, and possible priorities of a potential ARPA-H will continue to be discussed over the course of the congressional appropriations process, with consultation between the Legislative and Executive Branches. We encourage the CSPI community to serve as a resource for Members of Congress and their staffs to ensure that the new agency will be properly positioned to contribute to significant advances in human health and biomedical technologies.

Increasing equity and accessibility of research funds can help secure U.S. leadership in science

Just a small group of nationally-ranked universities are awarded the majority of federal research funding. In 2018, a study found that out of more than 600 colleges and universities that received federal funding for science and engineering research, about 22 percent received over 90 percent of the funds. The equity and accessibility of these funds was the focus of this week’s Senate Appropriations Committee hearing held to discuss the budget that could be allotted to the National Science Foundation (NSF) in fiscal year 2022. During the hearing, NSF director Sethuraman Panchanathan emphasized that addressing research disparities and establishing far-reaching partnerships were priorities for the agency.

Disparities in research funding

Disparities in research funding can greatly harm the ability of students to enter scientific careers, and diminish the potential of the country’s scientific workforce overall. The institutions that received over 90 percent of federal science funding in 2018 served only 43 percent of all students in the U.S., and only 34 percent of students from underrepresented groups. So two-thirds of underrepresented minorities and almost 70 percent of Pell grant recipients (who are undergraduates with “exceptional financial needs”) have more limited access to valuable opportunities to participate in scientific research. At the same time, researchers argue that incorporating diverse perspectives and talents leads to more innovative solutions, and that not including underrepresented minorities in science will only harm the U.S.’ competitiveness.

NSF’s most well-known program to address research funding disparities is the Established Program to Stimulate Competitive Research (EPSCoR). This program, which is now over 40 years old, partners with institutions of higher education to stimulate sustainable improvements in research and development capacity in specific states. States (as well as U.S. territories and DC) become eligible for EPSCoR funding if they receive 0.75 percent or less of total NSF research and related activities funding over the previous three years. Studies have shown that states with EPSCoR funding increase the quality of their universities’ publications, and that they become more competitive for future federal research funding competitions. However, more research needs to be done to fully assess the program’s impact.

Expanded access to research funding a priority for the Biden Administration

The Biden Administration has emphasized the importance of addressing research funding accessibility in the FY 2022 skinny budget request, which highlights the President’s top spending priorities for the next year in advance of the release of the full request for each agency. Specifically, President Biden is requesting $100 million for programs that “aim to increase participation in science and engineering of individuals from racial and ethnic groups, who are traditionally underrepresented in these fields.” This funding is intended to support increasing science and engineering research and education capacity at Historically Black Colleges and Universities (HBCUs) and other Minority-Serving Institutions (MSIs), as well as research on recruitment and retention methods, mentorship programs, and curriculum development. Studies by the National Academies of Science, Engineering, and Medicine (NASEM) have determined that this type of funding is critical to ensure the success of underrepresented minority students.

Director Panchanathan’s priorities for NSF

During the hearing, Director Panchanathan echoed (46:05) that more needs to be done to tap into the U.S.’ potential scientific talent. His two main priorities for NSF are to increase access to scientific research through regional innovation accelerators and to strengthen partnerships with other agencies, including the Department of Energy (DOE) and its national laboratories. The regional accelerators would rely on an expanded EPSCoR program, as well as support from other NSF directorates. NSF is also working to expand artificial intelligence (AI) research to every state to tap into as much talent as possible. Last year, NSF distributed grants to develop seven AI institutes which have operations in 20 different states. Director Panchanathan hopes (46:45) to expand this further in the coming years. This idea of widely-distributed hubs aligns with a new proposal from FAS’ Day One Project that suggests a path forward for the creation of innovation ecosystems that would launch new startup ideas and cultivate the next generation of research and development talent.

Regarding strengthening partnerships with DOE, NSF collaborates with the agency on a variety of programs, including the development of new algorithms to bolster the security and efficiency of modern power grids, the creation of collaborative robots to assist humans with a variety of tasks, and the advancement of basic plasma research and education. NSF historically focuses on basic research, while DOE, and its national labs in particular, drive the commercialization of new technologies. Director Panchanathan aims (1:22:06) to further develop relationships with the agency to more closely connect NSF’s basic research strengths with DOE’s expertise in technology transfer and ensuring cutting-edge research and technologies are commercialized in the U.S., instead of by other countries. By fostering closer cooperation between NSF and the other federal science agencies, the U.S. will be able to better compete with countries, such as China, that aim to supplant the U.S. as world leader in critical technology and science fields.

The future of research and development in the U.S.

Both the Biden Administration and Congress would like to accelerate science and engineering education and research to boost the U.S.’ domestic growth and global competitiveness. In the formulation of the FY 2022 federal budget for science funding, there will be more discussions on Capitol Hill about how to bolster the country’s expertise in high-priority fields such as AI, climate science, quantum computing, clean energy, and biotechnology, and harmonize the approaches of the executive and legislative branches. We encourage the CSPI community to get involved in future CSPI calls to action, and serve as a scientific resource for policymakers.

House explores the future of work at the close of the decade

Just before Congress left for the holidays, the House Education and Labor Subcommittee on Higher Education and Workforce Investment held a hearing examining ways to prepare for the future of work. This has become a hot topic this year, particularly as presidential candidate Andrew Yang has incorporated it into his platform and elevated it onto the national debate stage. The issue highlights the societal and economic changes that are underway due to the development of new technologies such as automation and artificial intelligence. These technologies will cause major shifts in the types of tasks performed and skills required in our occupations, as well as the creation of a host of new employment opportunities. However, with this growth, there are concerns that low- and medium-skilled workers could be displaced and left behind. The federal government has a long history of administering job training and reskilling programs for displaced workers but these new technologies present unique challenges.

We asked our scientific community to submit questions and important topics that should be discussed and we provided them as an online resource for Members of the Committee before the hearing. The insightful, data-driven submissions we received included questions about lifelong learning, the expansion of apprenticeships, the decline in funding for workforce development programs, the impact of automation on the workforce, and the roles of the public and private sector in helping workers adapt to the future of work. All of these topics were touched upon during the hearing. Agreement between Members of the Committee and witnesses was most apparent on how the current patchwork of federally-supported workforce development programs are not enough, and that their funding should be increased.

Chairwoman Susan Davis (D, CA-53) opened the hearing by critiquing the lack of federal investment in U.S. workers. She emphasized how the U.S. government spends only 0.1% of its budget on workforce development, while other industrialized nations spend an average of six times more. This can leave valuable workforce programs strapped for cash and harm workers looking for help in landing their next job. In fact, displaced workers are expected to navigate the confusing network of federal programs on their own, needlessly extending their search for assistance and a new job. Chairwoman Davis noted that reskilling alone will be insufficient to prevent worker displacement and that government programs should prioritize lifelong learning.

Ranking Member Lloyd Smucker (R, PA-11) added in his opening statement that the Taskforce on Apprenticeship Expansion was created to reduce the red tape and establish new apprenticeship programs. To understand the complexity of the federal training program landscape, the Government Accountability Office performed a study in 2009 and found that the federal government administers 47 different job training programs in nine different agencies. Many of the current retraining programs target specific categories of workers, such as those who have been laid off as jobs moved overseas or those who are underqualified, instead of targeting the training needs for specific types of work. However, studies like the Taskforce on Apprenticeship Expansion’s 2018 report have found that training and apprenticeship programs focused on developing the skills that local businesses need to succeed are often more effective than their current federal counterparts.

The statement that triggered one of the more compelling exchanges during the hearing came from former Acting Secretary of Labor, Seth Harris. He insisted that the US does not suffer from an inability to find workers with the right skills, often called the “skills gap.” If there was an actual gap between workers’ abilities and the skills needed to succeed in the workforce, wages would dramatically increase for workers with the right skills and employers would spend more money on training their employees to learn those skills. This has not happened. He explained that the skills gap argument blames workers for not knowing what skills would be in demand when choosing an education, instead of acknowledging a systemic disconnect between degree and certification processes and employers’ needs, the lack of apprenticeships, and reduced funding for on-the-job training.

When Representative Mark Takano (D, CA-41) asked what Congress can do to help, Mr. Harris advocated for more transparency in the credentialing system and stronger Trade Adjustment Assistance Community College Career Training (TAACCCT) programs to help people get the right skills to succeed in the workforce. There are thousands of programs that claim to help workers earn certifications in sought-after skills; however, there is little data on which programs are actually effective. More transparency into the success rates of these programs would allow workers to enroll in the best programs for their career plans. The Department of Labor’s TAACCCT program began in 2011 and awards grants to community colleges to improve their curricula “to help adults learn skills that lead to family-sustaining jobs.”

The creation of learning savings accounts for workers was also the subject of vigorous discussion. James Paretti, Treasurer for the Emma Coalition, emphasized that the biggest challenge will be for both employers and employees to understand that some displacement is inevitable and workers must be prepared. Stockpiling funds is one way that workers could automatically save for their future education and weather employment challenges. A variety of learning savings account models have been proposed, with workers, employers, and the government all having the option to contribute funds at assorted levels, similar to the contributions made to retirement accounts.

This hearing covered a lot of ground, but Members have not completed their fact-finding into the future of work. Chairwoman Davis announced that her Committee will be holding another hearing about this critical issue. As Congress prepares to dig deeper into the future of work, we encourage you to email any data-driven questions or workforce topics that should be discussed to sciencepolicy@fas.org.