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The Venture Equity Project Podcast is a storytelling platform where we bring on the world’s most incredible entrepreneurs, forward thinking academic, nonprofits, and venture partners to talk about how we can take steps to fix the complex problem of inequity in venture. Host Weilyn Chong will help us gain insight on everything from how we can increase access to capital for underrepresented entrepreneurs and tips on getting in front of VCs and angels.
In this week’s episode, Max Crowley, Director of the Evidence-to-Impact Collaborative at Penn State University, dives into what it means to use data for good. Max shares what the Evidence-to-Impact Collaborative is all about, how we can use data to create a more equitable venture future for entrepreneurs of color, and how human development research relates to the entrepreneurial landscape.
The Nasdaq Entrepreneurial Center is proudly hosting and producing this podcast. The Center is a non-profit that is building a better path for entrepreneurs worldwide by improving inclusion, access, and knowledge in entrepreneurship.
Weilyn Chong: Hello and welcome back to the Venture Equity Project Podcast. I’m your host, Weilyn Chong, and I’m so excited to be joined by Max Crowley today.
WC: We’re gonna jump right in and talk about the Evidence-to-Impact Collaborative. Can you give me a brief overview of what the collaborative is and what the main mission and motivation behind it is?
Max Crowley: Absolutely. So, the Evidence-to-Impact Collaborative is a center here at Penn State University, and its goal is to build the science of scientific impact. And we do that by working to increase the relevance, value, and use of scientific evidence. In particular, we think about, “How do we support the use of research in settings all across society, from government, industry, from a local level to an international level?” But fundamentally, we believe that there’s this opportunity that through the use of scientific evidence, we can create a better, more just, more equitable society.
WC: I want to touch a little bit on how this is touching entrepreneurship and the venture equity space.
MC: Absolutely. So, entrepreneurship is an engine for so many aspects of our economy, but also is a tremendous opportunity for so many to create not only personal wealth, but intergenerational wealth. It has been denied to many in our society, both here in the States and around the world, and so the opportunity to support creating an ecosystem for entrepreneurs that is driven by evidence, driven by good scientific insights around how we support entrepreneurs regardless of identity. It’s key not only to supporting those individuals, supporting a stronger economy, but doing so in a way that focuses on building intergenerational wealth that will transcend any individual’s lifetime.
WC: What does the current state of venture capital flow and entrepreneurs of color’s experience in the current entrepreneurship landscape [look like]?
MC: So, access to capital is currently a huge issue for entrepreneurs of color and also female-identifying entrepreneurs, particularly in the States. So, there’s clear inequities in terms of how that access is made available or how the game is run, and as a result, there is a fundamental need for the entire entrepreneurial space to take a hard look at ourselves around, “How do we distribute resources in a way that both leads to success of the investment, but also recognizes the need to consider equity within the entrepreneurial space?”
WC: In what ways can data help us edge forward to making a more equitable space for entrepreneurs of color?
MC: There’s a number of different ways, but the most fundamental way is that we don’t really understand the space. There is a lot of information that not only is not even public, but it’s not even available to the major stakeholders and players who could actually enact change. So we don’t have a good sense of the totality of the inequity in some of these areas. We have clear signals and clear information that it’s there, but we don’t know exactly how it manifests, and maybe more importantly, we don’t actually know where it’s originating from within what we conceptualize is really a system. It’s not really about individual actors — individual actors are of course an important point of intervention, if you will – but it’s really about a larger system that is oriented in a way that doesn’t support all entrepreneurs, and that’s what we’re particularly focused on understanding: How do we fill those gaps with the fundamental information we need to understand the ecosystem?
WC: So really, the objective here is to get to the core of all of this. What are the steps that the EIC is taking to tackle this large problem?
MC: There’s a real opportunity here, along with our partners at the Nasdaq Entrepreneurial Center that we’ve been engaging in, and that is to map the data space as it relates to not only entrepreneurship, but the larger entrepreneurial ecosystem with a focus towards understanding how resources flow and identifying areas where there are inequities where resources are distributed; not because of a promising venture, but because of either fundamental biases, or maybe moreso because there’s just a lot of systemic barriers stacked against certain entrepreneurs with certain identities. So that’s a key opportunity, is just understanding and bringing that data forward. But of course, as you might imagine, that requires engaging with a [?] of stakeholders. We’re working across the ecosystem to get them to share. That’s what it’s about. How do we share information in a way that makes people comfortable and allows us to unearth these issues while at the same time not forcing individuals into data sharing arrangements that are counterproductive for their mission and organization.
WC: That’s so interesting, the process of collecting that data in a way that’s comfortable for so many stakeholders. What’s been unexpected that you’ve learned about the entrepreneurial landscape that you didn’t know before?
MC: Oh, well, there’s quite the list, right? But I think the most fundamental aspect is: It is about the people, at the end of the day, but it’s all within systems. So one of the things that we’ve loved about our partnership with the NEC, the Nasdaq Entrepreneurial Center, is their perspective and focus on the whole entrepreneur. And that really aligns closely with the science of human development at its core. People live within families, and those families live within communities, and those communities live within societies. And in the entrepreneurial space, entrepreneurs work within their company or the venture that they’re trying to create, but that sits within these larger systems, and so recognizing that people are the actors but people are acting upon each other. And so seeing that parallel so starkly in this space really reminds us that entrepreneurs are people and investors are people, but they operate within systems that may or may not support their goals and they may or may not support goals for larger justice and equity-related initiatives.
WC: Entrepreneurs are people at the forefront, and I really love that kind of visualization of: They’re people interacting and acting on each other. In what ways can we allow data to help us make more meaningful strides towards a more equitable venture space for entrepreneurs of color?
MC: Yeah. That’s a really good question. So the most tangible element of all of this is, we need to understand where the resources are going, where the resources are flowing, and we need to understand not only to which people, but how those people identify. And that’s really key. Because identity is a complex thing, right? Racial and ethnic identity is of course only one part of an individual’s larger identity. It’s an important one, and an area that equity often hangs around, but it is not the only area of identity. So understanding from the entrepreneur how they identify is incredibly important, and we know that identities also evolve across time in some spaces. And some identities are very fixed, and we carry through our entire lives. So having a better understanding of where resources are flowing and to whom they are flowing is key for really peeling back the onion, if you will, and taking a peek behind it to understand what we can do. To your question, what that data allows us to do is, it’s not only to observe, it’s to act. Because in order to really bring about systemic change that’s lasting and durable and works for everyone, we have to think about tailored strategic intervention. We can’t just rely, for instance, on government to make blanket regulations in this space — that doesn’t work, especially when we’re focused on innovation economy-related issues. There is government, but a broad blanket regulation doesn’t really help in this space. It does require the entrepreneurial community and the investment community to take a hard look at what they want to be and who they want to be, and how they want to invest their time and energy to intervene. But to do so requires knowing where to intervene most effectively, when, and with whom. And that’s only something you can do with the data.
WC: That makes a lot of sense. What is the future after the Evidence-to-Impact Collaborative works on the entrepreneurial space?
MC: Well, we definitely have idealized futures that we hope to see come about, but as you might imagine, this is something that occurs in fits and starts. But in a realistic but still somewhat best case scenario, we see broader buy-in from key leaders and stakeholders within the entrepreneurial community to say, “It is important that we share this data for this fundamental reason that if we do it, others will.” Maybe then government will step in and try to regulate us. But if we can show and demonstrate that we can support entrepreneurs and deal with some of these fundamental aspects of inequality, then the entire space will be better off for it, and thus it’s worth taking the time and the energy and the risk, right, to share information with each other so that we can have a better understanding and a top-level view and that there can be openness and transparency in these spaces. Which as you know, in the venture space in particular, there’s not necessarily a culture of openness in many ways because it is competitive. So that of course is probably one of our biggest barriers: How do we inject a level of collaboration for the overall good in a space that’s of course hyper-competitive. That’s sort of the rub, if you will.
WC: For sure. Could you talk a little bit more about how you guys are overcoming that barrier? I definitely resonate with this idea that the venture equity space right now is very hyper-competitive and very vague and abstract, almost. So how are you guys able to bring collaboration to the forefront of this project?
MC: I think that it’s really important to say that the EIC and the EIC’s partnership with the NEC cannot take credit for what is fundamentally a movement in this space. There are others who fundamentally recognize that this is an issue. What we bring to this space is this focus on data and some of the tools and resources to maybe demonstrate what this new world could look like. That being said, what it fundamentally is about and what those who came before us and those whose shoulders we stand upon have demonstrated through and through is that it’s about relationships and it’s about building trust, right? In any competitive space, if you can’t build trust, then you’re sort of dead in the water. But building trust often requires first having a common goal, and so that’s I think where we’ve, you know, seen some real signs of hopefulness in this space; is that there are many groups and many individuals that are championing within their own organizations, within their own firms, the importance of this work, of paying attention to this fundamental societal inequities, and those change agents — every single one of them — are part of this effort, fundamentally. So I would say that is one of the key lessons learned, and certainly would not and can not take credit for what we believe is a movement. And what we hope to do moreso is throw gas on that fire, if you will, by providing them with the insights through quality data that are needed for those change agents to make more strides.
WC: That makes a lore of sense. For this project specifically and the entrepreneurial space, why is a dynamic data infrastructure key to creating a more equitable venture future for entrepreneurs of color?
MC: So first we should unpack what we mean by “dynamic.” Fundamentally what that means is it’s living. It’s alive. Just like the people and the organizations within the space. So a snapshot is only going to tell us what’s wrong. It’s not gonna tell us how we can fix things and more importantly, it’s not going to tell us if it’s working, if the things that we’re putting in place are working. So dynamic data really implies this idea that we are capturing data streams from a variety of different places, using them responsibly, and bringing them together to have the whole picture. And then as we do things to support the community, we look at, “Have we actually made strides to reducing inequity?” And that’s the key of it being dynamic, because otherwise we can’t benchmark ourselves. And to be completely honest, historically, in lots of spaces — including the entrepreneurial space — where people have sought to reduce inequality or attend to issues of inequity, it’s so hard to know if you’re succeeding that those movements, those efforts, whatever space they might be in, can quickly lose steam. But if we can demonstrate that we can qualitatively and quantitatively make things better, we have so much more opportunity to keep this momentum and build this movement across time.
WC: That’s amazing. What are some of the questions driving the research that you guys are doing? What are you doing to dig deeper into all of these different components of the infrastructure that you’re trying to build?
MC: So, what we’ve had the opportunity to do with data that we’ve received from a variety of stakeholders across the entrepreneurial space is to take a hard look at what the individual is experiencing. What the founder is experiencing. And we do that both qualitatively and — maybe more important in this setting — quantitatively. We look at not only their funding history and the history of what they’re trying to build, but we also look at it in a context of both their racial identity as well as some of these structural elements that we were talking about earlier. For instance, the community that they’re living in; the neighborhoods that they’re living in; how they as an individual are building or have had access to personal wealth. And those are key elements for building a larger analytic model of what happens over time to a founder. And that’s key, because by doing that, then we can start thinking about how founders of different identities have different experiences. And then we can ask the question of why.
WC: I love how you guys are mapping individual entrepreneurs’ experiences and then also comparing and seeing patterns between those experiences. So, what data — as of right now, or when the project started — has already been done in the entrepreneurial ecosystem, and what are some of the major gaps that we’re trying to fill with the collaborative project?
MC: Some of the data that is available through different proprietary channels but can be purchased or accessed by members of the entrepreneurial community does a really good job on capturing the publicly-accessible information, right? Stuff that has to be filed with the SEC or otherwise. It also does good work on trying to capture certain aspects of a founder’s identity. Where it of course struggles is particularly around racial and ethnic identity, as well as some other key identities that have been typically marginalized in this space around gender or sexual minorities. So there’s a real opportunity here to think about how we fill in those gaps. And there’s good reasons that those identities aren’t necessarily just part of mandated information gathering because of histories of marginalization. There were concerns that those individuals will be discriminated against because of a certain identity. At the same time, this is why it’s so important to build a community that trusts each other when it comes to handling and use of this type of data. There’s a number of different ways to protect those individuals’ personal information and look at things in the aggregate once that data, of course, is in place. So the need to understand these key identities is really important and a major gap in these spaces, and there’s a number of strategies that could come online to support this work, but it really still takes the buy-in from stakeholders both in the investment community, the entrepreneurs themselves, as well as the many stakeholders who support and accelerate and incubate different founders.
WC: I think the approach that you guys are taking is very much centered back to what we were talking about earlier: The idea that the entrepreneurs and all of the different stakeholders are, at [their] core, they’re people. So, the Evidence-to-Impact Collaborative works on multiple different projects. What are some of the key similarities and differences between the project that you’re working on in the entrepreneurial landscape compared to other research projects that you guys are working on?
MC: We work in a number of different spaces. We work with practice communities, we work with policy communities, we work in international settings as well as domestically. And I can say that a uniting theme across all of them is that leaders, in whatever space they are, generally want to use scientific information. They want to use data to make decisions. But they don’t have the capacity to access and translate that information on their own. So for instance, we work with policymakers from the local level to state government all the way up through to members of Congress. And we think a lot about, “How do you support very busy, low-bandwidth leaders actually using scientific evidence?” About two years ago, we started a randomized control trial of, in this case, members of Congress, but we randomized members of Congress to receive supports around how they could use data and how they could use scientific evidence to make decisions and to create policy. And we were able to find that by providing key supports — the same kind of supports that we hope to provide in the entrepreneurial space to leadership — that we could fundamentally increase their use of research evidence, but also support them in executing on their policy goals in ways that led to better outcomes for their constituents. It’s really a nice parallel because we believe that if we can do it in a place such as Congress, we can definitely do it in places that already are embracing data in many other spaces. And so I think that’s a really important, nice thing that we’ve been able to see across government, across practice communities, and across the entrepreneurial community as well.
WC: I guess to end off this podcast today, I’d love to ask you: What does it mean to you and to the Evidence-to-Impact Collaborative to use data for good?
MC: I think there’s the lighter answer and then there’s the deeper philosophical answer. And I’ll give you a little bit of a mix of both. Fundamentally, what data is is information, right? Data is neither good nor bad, but it can be used for good or bad purposes and lead to good or bad outcomes. And so using data for good, or data for good in general, is fundamentally about recognizing that your goal may be well-intentioned, it may seek to do good, but the good that you perceive it to be may not be good for everyone, and there are also potentially unintended things that happen when you try to reach your goals. And so using data responsibly, using it for good, is not only using it for good intentions; it’s about thinking about how those individuals whose data you are engaging with will perceive and experience whatever happens as you use their data. Not just at the endpoint; all along the process. So it requires not only thoughtful engagement around security and data privacy, but also an ethical and moral reckoning that every time we pick up someone’s data, we have to think about, “What are the implications of that?” And that’s key, because if we don’t do that, we could achieve something that we perceive to be good while hurting a lot of people along the way. So data for good is as much a process and a mentality as it is an end goal.
WC: Wow. I never really thought of data in that aspect; this two-way street that you have to not only use the data to produce good, but also realize that the way you’re interacting with that data in the first place is a good interaction and keeps a person in a good light. Thank you so much for joining us on the Venture Equity Project Podcast. For our audience who want to continue to follow your journey and follow the Evidence-to-Impact Collaborative, how and where can they do that?
MC: You can certainly follow us on Twitter at @evidencetoimpact, and you’re welcome to check us out on our website by the same name within Penn State.
WC: Awesome! Thank you so much for joining us on the podcast, and we can’t wait to see what you guys are doing in the future.