https://orcid.org/0000-0002-0658-5367
Figures
Abstract
Prior scholarship generally examines the returns generated by firm-government engagement. These studies are based on an implicit and understudied assumption – the firm’s strategic choice of whether to engage with the government. Here, we unpack the drivers of this choice. To do so, we construct a population-level sample of U.S. high-tech ventures founded between 2015–2017; the full sample exceeds one million firms. We then utilize government records to identify initial firm-government engagement; approximately 24,000 high-tech ventures reveal this preference by firm age three. We examine a range of external and internal factors that may motivate such a choice. The results indicate that firm-government engagement most prominently coincides with firm resource constraints. Features driving such engagement include: (i) underrepresented minority-owned firms; (ii) small firms; (iii) firms with greater early-stage growth potential; and (iv) firms located in less intensive entrepreneurial settings. This study offers managerial, policy, and scholarly contributions by uncovering new insights around firm strategy and government opportunities for high-tech ventures.
Citation: Lanahan L, Hemmatian I, Joshi AM, Johnson EE (2025) Drivers of firm-government engagement for technology ventures. PLoS One 20(10): e0333710. https://doi.org/10.1371/journal.pone.0333710
Editor: Silvio Eduardo Alvarez Candido, UFSCar: Universidade Federal de Sao Carlos, BRAZIL
Received: December 16, 2024; Accepted: September 17, 2025; Published: October 10, 2025
Copyright: © 2025 Lanahan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data, code, and log files are available from the Harvard Dataverse database. The citation is as follows: Lanahan, Lauren. 2025. “Replication Data for: Drivers of Firm-Government Engagement for Technology Ventures.” Harvard Dataverse. https://doi.org/doi:10.7910/DVN/KT6136.
Funding: This research is funded by the National Science Foundation’s Science of Science Program (NSF 2032914), a Kauffman Foundation Junior Faculty Fellowship grant, and research funding from the Wake Forest University School of Business and Wake Forest University Center for Healthcare Innovation, School of Medicine.
Competing interests: The authors have declared that no competing interests exist.
“We were a very young company. We had no clue on what to do or how to get started”,
The four partners spent the beginning years developing a plan for the company and contemplating if they would focus on the private or government sector. After seeking the help of the Florida Procurement Technical Assistance Center at the University of South Florida (USF), Matt Cetta, a founding partner of SBS (Strategic Business Systems), said…
“[We] chose government.”
Small Business Development Centers (SBDC), Success Stories [1].
Introduction
Technology ventures turn to the government to secure direct funding resources [2] and to access indirect benefits such as technical expertise [3,4], legitimacy [5–7], and market validation [8]. Uniquely, these firms tend to work on projects with outsized technical and time uncertainties [9], producing inherent high risk and increasing the likelihood of failure [10]. As a result, private financiers are more hesitant to provide the necessary financial resources [10–12].
To overcome these resource constraints and support the growth of young technology ventures, the U.S. federal government offers an array of public programs spanning local training and assistance, funding, and procurement contracting [13,14]. For example, in 2022, the U.S. federal government invested approximately $177 billion annually in R&D programs to support firms and market activity [4,15]. Ideally, public funding serves a distinct role because of the high-risk, uncertain nature of investments in basic scientific discovery [11,16,17]. In short, government investments persist given that the private sector lacks incentives to undertake these investments themselves due to the risk-return profile and time horizon [18,19].
Prior scholarship devotes considerable attention to examining the returns of these various government programs [14,20,21]. And depending on the outcome of interest – ranging from innovation to commercialization – the returns are mixed, putting to question whether government programs in fact serve as a complement or a substitute to market activity. On the one hand, numerous studies document favorable returns of firm-government engagement on innovation [22–24]. Yet, other studies find that these government programs can produce market distortions and decrease firm performance [25–28].
While the debate around the value of government returns persists, studies on both sides rely on an implicit and understudied assumption – the firm’s strategic choice of whether to engage with the government. In other words, despite the prevalence of government programs and corresponding scholarly attention examining firm output generated from public sector resources, such a context depends on the firm electing to seek these resources in the first place. However, somewhat surprisingly, no large-scale empirical studies directly examine the fundamental drivers of this strategic choice. Most often, scholars treat this firm-level decision as an underlying assumption and discuss the subsequent implications of this endogenous feature in the econometric model. This issue is most often addressed using matching techniques [29], regression discontinuity designs [30], selection models [27], or exploiting shifts in programmatic oversight [31]. The lack of prior research in understanding the underlying drivers of this relationship is problematic because the dearth of evidence can lead to missed opportunities for entrepreneurs and managers, ineffective policy design by policymakers, flawed theory development, underspecified research designs, and inconsistent recommendations by scholars.
To address these issues, we examine this core assumption by evaluating a range of antecedents that motivate young high-tech ventures to pursue firm-government engagement, which we formally define as firm initiation and engagement with the government to seek resources. These resources include funds available via contracts, grants, and loans, as well as training, mentoring, and other support. Specifically, our study investigates the following research question: what are the drivers of firm-government engagement for technology ventures?
To analyze this question, we construct a population-level sample of U.S. high-tech ventures founded between 2015–2017; the full sample exceeds one million firms. We then utilize government records to identify initial firm-government engagement. This comprises the sub-sample of approximately 24,000 high-tech ventures with a revealed preference in expressing any formal interest in establishing a relationship with the government over the firm’s first three years of operation.
To unpack the antecedents that plausibly drive this strategic firm choice, we examine a range of external and internal factors for the firm. The former includes three sets of ecosystem indicators: institutional intermediaries, capital infrastructure, and entrepreneurial intensity. Additionally, we include measures of political context and government opportunities. The latter set includes features of firm imprinting and early-stage growth.
The results are compelling and highlight salient trends that guide the strategic choice of firm-government engagement. First, the descriptive statistics reveal that approximately 2.3 percent of U.S. high-tech ventures establish such engagement. Second, the correlative regression results highlight that firm-government engagement appears to substitute for market opportunity. In other words, high-tech ventures with limited access to resources are more likely to seek such external engagement. Accordingly, we report that firms with underrepresented minority (URM) owners, small firms, and firms with greater early-stage growth potential (measured by credit and patent activity) each increase the likelihood to seek such engagement. Moreover, industries with heightened levels of federal contracting attract such engagement. Conversely, the substitutive effects most consistently include firms located in less intensive entrepreneurial settings, which lack adequate resources for ventures. This study offers managerial and scholarly contributions by uncovering insights around firm strategy and government opportunities for high-tech ventures seeking resources. The results provide empirical findings for guiding evidence-based policymaking by quantifying the extent to which government programs support ventures.
Conceptual framing
Early-stage technology ventures engage with the federal government for various reasons. For example, it is well documented that ventures are resource-constrained and face inherent liabilities due to their “newness” and “smallness” [32–34]. Moreover, such uncertainty is amplified in high-tech sectors where the innovative frontier is constantly changing and accelerating [12]. Accordingly, innovation takes a long time to develop, and it has a high level of costs and risks coupled with persistent uncertainty [35]. Consequently, these challenges can hinder ventures’ survival and long-term growth [36,37].
Government programs offer one alternative for ventures at this juncture to traverse myriad early-stage challenges that include the infamous “valleys of death” [38]. Here, business operations are underway, and substantial costs are incurred but meaningful revenues have not yet materialized. Firms may seek government support to overcome these resource constraints as they pursue various growth opportunities in both private and public sector markets. Though the extensive research thus far on the relationship between government programs and market activity reveals mixed results. Some studies indicate complementarity of the programs, while others uncover substitution effects [39,40].
To understand the antecedent factors that drive young high-tech ventures to establish an initial relationship with the government, we draw from literature streams on entrepreneurial ecosystems [41–43], resource dependency [29], entrepreneurship [44], and innovation [45]. Fig 1 illustrates our conceptual model, which in turn guides the empirical specification. In the context of young high-tech ventures, external and internal factors that influence firms to seek strategic engagement with the government remains an empirical question [46,47]. Ultimately, these features could be crucial predictors of long-term performance, growth, and survival of technology startups.
System for Award Management (SAM). By design, the administrative data repository of SAM is comprehensive and identifies the event when a firm elects to initiate engagement with the federal government as a potential grant recipient, supplier for procurement contracts, or borrower of loans.
And to be clear, this study is exploratory. We do not present hypotheses or propositions articulating whether these various factors amplify or attenuate the strategic choice of the firm to engage with the government. Instead, we argue that internal and external factors place pressures (of varying degrees and directions) on firms that determine when and how young technology ventures strategically engage with federal government programs [48]. Moreover, this study is descriptive; we aim to understand whether and how various factors drive this choice. By design, we are unable to make causal claims as we are examining an endogenous process. In the following section, we define and review each feature under consideration.
External factors
Entrepreneurial ecosystems consist of interconnected actors and elements that are organized to establish environments that support productive economic activity [49,50]. They play a central role in fostering technology-based ventures and defining the local landscape within which they operate [43,51,52]. These ecosystems involve complex interactions between young high-tech ventures, support organizations (e.g., universities, training institutes, and scientific parks), and other stakeholders (e.g., public sector and government entities) [53,54]. Drawing from this conceptual lens, we identify three ecosystem indicators that may motivate the venture’s choice to express formal interest in establishing a relationship with the government.
First, institutional intermediaries shape venture access to prominent organizations that serve as ecosystem-wide business leaders and advocates for the R&D enterprise [55–57]. For instance, when launching new high-tech ventures in a specific region, entrepreneurs establish mutually beneficial relationships with scientific communities to acquire knowledge and skilled human resources [58]. Research-active universities, incubators, and accelerators play a critical role in this regard not only by providing administrative and technical leadership, but also through extensive investments in human and physical capital [27,59,60]. Young high-tech ventures may use intermediaries, particularly research universities, to indirectly access government resources and knowledge networks [61,62]. By making these resources accessible, universities become vital hubs in supporting and fostering entrepreneurial ecosystems, especially in resource-constrained environments [63].
Second, capital infrastructure represents the breadth of financial capital that is accessible to ventures at their early stage [64]. Financial access is crucial for entrepreneurship and the development of entrepreneurial ecosystems [65]. This includes funding opportunities available via banks and local lending institutions [66] and the more competitive (and sizable) resources available via private financing [67]. Financial resources play a pivotal role in the entrepreneurial ecosystem and subsequent growth and innovation of technology ventures [50]. Availability of financial capital strengthens entrepreneurial ecosystems; in turn, strong ecosystems directly attract and efficiently allocate financial resources to new ventures [68,69]. Studies report that effective ecosystem development benefits from leveraging both government and private financial resources [70,71].
Third, entrepreneurial intensity accounts for the rates of startup activity and degree of market competition in an area. Heightened levels of such activity can bolster agglomeration benefits for ventures through common links of knowledge, capital, inputs, and demand [72,73]. High intensity enhances entrepreneurial ecosystems by attracting more talent and investment and encourages positive regional economic development and national policy reforms [74,75]. A high level of entrepreneurial activity is crucial for driving innovation, economic growth, and the overall health of entrepreneurial ecosystems [76,77]. In vibrant entrepreneurial ecosystems, entrepreneurial intensity signals market potential and attracts complementary resources and enhances knowledge sharing among ventures, especially in high-tech sectors where systemic innovation is prevalent [78,79].
In addition to the set of ecosystem indicators, political factors can play a significant role in shaping entrepreneurial ecosystem governance through policy formation and implementation [80]. This role is particularly important given that the resources are obtained through firm-government interactions. Research reports that firms maintain alignment with changing government policies and ecosystem dynamics through adaptive approaches [81]. Political context comprises both the political ideology in the local region and alignment between local and national politics; both may influence the extent to which ventures consider engagement with the U.S. federal government [52]. Moreover, government support for ventures and technology is not only guided by national efforts, but also public support is locally tailored via state government policies and regional initiatives [53,82]. Overall, politics plays a significant role in shaping entrepreneurial ecosystems, where political dynamics and government policies can foster or hinder entrepreneurial activity [83,84].
Lastly, we consider government opportunities. Government spending programs help support entrepreneurial ecosystems by offering funding, infrastructure, education, and policy measures [51]. This includes regulated programs designed to bolster resource access to new ventures [51]. Young high-tech ventures may strategically utilize specific government programs to support their innovation, growth, and survival [2]. Additionally, certain industries are of greater interest to government projects and initiatives [22]. U.S. government contracting is an immense enterprise. For example, in 2019, the U.S. Department of Defense spent 403.9 billion USD on procurement contracts with firms. This level of activity represents approximately 73 percent of the agency’s total annual spending, or nearly 2 percent of U.S. GDP [85]. Trends in government contracting likely shape opportunities and may motivate ventures to develop a relationship with the government.
Internal factors
We draw from the entrepreneurship, resource dependency, and innovation literature streams to identify internal factors of firm-level imprinting [29,86,87] and early-stage growth potential [88]. We argue such features are likely to motivate a venture’s choice to express formal interest in establishing a relationship with the government. Firm imprinting includes founder demographics [20,44], whereas growth captures the early potential for a venture [89].
To elaborate on the former, studies report that firm founders’ demographic backgrounds and prior experiences strongly influence a technology venture’s propensity to pursue federal government opportunities [90]. These characteristics and past professional experiences impact whether entrepreneurs perceive interaction with the government as beneficial, accessible, or aligned with their venture goals [91,92]. As a salient example, the Small Business Administration (SBA) procurement scorecard is an annual assessment that measures how well federal agencies achieve small business contracting goals set forth by government statute [13]. Each year, a portion of both the prime and subcontracting goals are set for minority-owned, women-owned, or veteran-owned businesses. Minority founders or those who have previously served in the military often view government engagement as a familiar process and are more likely to establish the relationship from the start.
As for the latter, early-state growth orientation likely affects whether firms elect to engage in government opportunities as well. New ventures often face resource constraints that make them dependent on external support, such as government programs [93,94]. Of note, the size (and age) of the firm is correlated with resource constraints [89,95]. From the resource dependency perspective, this relationship shapes how new ventures operate, survive, grow or strategize [96–98]. The venture’s initial resource endowment likely impacts their decision to solicit external resources. When a firm heavily depends on specialized funding sources or third-party validation that private investors hesitate to offer, government programs like the Small Business Innovation Research program become attractive partners because they provide non-dilutive R&D funding, legitimacy, and/or technical expertise that can potentially unlock subsequent private financing [6,51]. Additionally, patenting defines a strategic role among high-tech endeavors as firms seek to offset the high risk and uncertainty tied to innovative pursuits with formalized contracting [45]. Patenting signals the novelty, usefulness, and commercial potential of a venture’s intellectual property and may demonstrate the venture’s ability to mitigate and overcome technical risks [99]. Recent scholarship is beginning to uncover differential positioning for young and small firms engaged in patenting endeavors, reporting a unique role of government in supporting such activity [100]. The extent to which a young high-tech venture is resource-constrained – based on internal strengths and capabilities – plausibly influences whether it seeks external government engagement [29].
Empirical context
Sample and key dependent variable
Directly answering our research question requires that we begin with a population-level sample of U.S. young high-tech ventures. Thus, we draw from the full repository of establishments listed in the National Establishment Times Series (NETS) database. NETS is collected by Dun & Bradstreet, which traces credit activity in the U.S. This proprietary source includes over 82.4 million U.S. establishments from 1990–2020 and reports annual detail on a range of characteristics that include location, industry, and performance measures. This database has been used by scholars [101,102] and has been found to provide reasonable estimates compared to restricted administrative government records of U.S. firm activity [103].
The sample of targeted firms share the following features: (i) U.S. owned; (ii) operate in high-tech industry sectors as defined by the Bureau of Labor Statistics [104]; (iii) are single-establishment organizations; and (iv) are founded between 2015–2017. Regarding the last specification, we set this timeframe due to the following. First, 2015 defines the initial year that the administrative source to discern whether firms express interest with the federal government (i.e., as reported in the U.S. General Services Administration’s System for Award Management (SAM)) is publicly available. Second, 2017 defines the last year to trace firms over a standardized three-year window that precedes the outbreak of COVID-19. This global event introduced unique confounding factors driving interest in government engagement that lie outside the scope of this analysis [105]. Altogether, this defines a total population of 1,014,868 unique firms nationwide.
To validate the completeness of NETS, we compare the level of single-establishment ventures reported in NETS to new registrations reported in every Secretary of State (SoS) repository over a comparable timeframe (2015–2017). This includes ventures in all industries (i.e., beyond high-tech industry sectors). Overall, NETS reports detail for 88 percent of U.S. startups reported in the SoS repository. This result attests to the completeness of the NETS data source and its suitability for our research design. And for further insight into the sample, approximately 12 percent operate in high-tech industry sectors. To illustrate regional variation, we report statistics by state in Table 1 (Columns 1 and 2).
As previously mentioned, we draw upon the U.S. General Services Administration’s System for Award Management (SAM) database to identify the population of firms with an expressed interest in transacting with the U.S. federal government [106]. By design, this administrative data repository is comprehensive and identifies the event when a firm elects to initiate engagement with the federal government as a potential grant recipient, supplier, or borrower. Effectively, registration in SAM is a revealed preference, which is the initial strategic “stepping stone” for firms to engage in federal programs by either participating in training or assistance, applying for R&D subsidies, or seeking contracting opportunities. (In other words, this includes all firm applicants seeking such government services and those that ultimately secure the resources.)
Table 2 details a range of government programs firms can access after registering in SAM. The Small Business Administration (SBA) leads these efforts, serving as a federal executive branch agency to support entrepreneurs and small businesses and help them start, build, and grow businesses. The SBA offers a variety of services, including financial assistance, counseling, and contracting opportunities. The programs are designed to provide comprehensive support to small businesses at various stages of their growth and development.
All entities registered in NETS and SAM are required to obtain a unique DUNS Number (Data Universal Numbering System) from Dun & Bradstreet. We use this 9-digit identifier to match firms precisely across each source. Based on founding dates, 92.9 percent of firms listed in SAM matched the NETS sample. Moreover, to examine strategic behavior at an early stage, we identify registration in SAM by firm age three for the entire population of U.S. high-tech ventures. The three-year timing specification both defines a standardized window to avoid right-censoring and precedes the global outbreak of COVID-19. Nationally, 2.3 percent of U.S. high-tech ventures (23,603 firms) register in SAM in this timeframe; we report trends by state (Table 1 Column 3).
Fig 2 illustrates the geographic distribution of this sample by county. Panel A depicts the distribution for the population of U.S. high-tech ventures (1,014,868 firms); Panel B depicts the sub-sample that register in SAM by firm age three (23,603 firms). These images illustrate the geographic reach across the U.S. (Panel A correlates highly with population density); however, Panel B depicts the concentration (or lack thereof) of such firm-government engagement in certain areas. The metropolitan area around the national capital accounts for a disproportionate amount of firm-government engagement; we confirm this with the leading trends for DC, VA, and MD reported in Table 1 (column 3).
Maps illustrate aggregate count of U.S. high-tech ventures by FIPS (Federal Information Processing Standards county code) founded from 2015–2017. Panel A reports the population of high-tech ventures (1,014,868 firms); Panel B reports high-tech ventures entering SAM by firm age 3 (23,603 firms). This map is based on Mapbox tools and data, with OpenStreetMap. It was made using Tableau Desktop Software v. 2024.1.
Regressors
Beginning with external factors, we construct the set of ecosystem indicators by leveraging features of geography. Importantly, high-tech activity is especially reliant on spillovers [24,107] and agglomeration economies [108–110]. These valuable externalities have essential locational components. For example, it is well documented that knowledge spillovers are most pronounced within short geographic spans (i.e., within a given county) and that local industry structures impact access to common resources such as labor pools and supply chains [111,112]. We leverage geographic proximity to various institutions as single-establishment ventures are likely to be especially reliant on their local surroundings. Contrastingly, older firms (which we exclude from this study) are less constrained by their proximate geography given their greater access to resources and multi-establishment positioning (e.g., setting up branch offices to serve new customers).
For institutional intermediaries, we identify ventures located within the median sample distance to their proximate university and accelerator. To derive this indicator, we rely on the locations of two comprehensive lists of U.S. organizations – universities designated as research-active by the Carnegie Classification of Institutions of Higher Education and accelerators [113]. Based on the physical address of the firm and organization set, we compute the nearest distance (in kilometers) between each firm in the sample and each organization type (i.e., nearest distance between venture and university and nearest distance between venture and accelerator). We assign a value of one to ventures with closer proximity to both universities and accelerators as we assume they have greater access to these institutions.
We apply a similar methodology for capital infrastructure. Here, we identify ventures located within the median sample distance to their proximate bank or local lending institution, angel investor, and venture capital organization. To derive this indicator, we rely on the locations of entities in three comprehensive lists of U.S. organizations – Federal Deposit Insurance Corporations (FDIC), angel investors, and venture capital (VC) organizations.
For entrepreneurial intensity, we identify ventures located in counties with startup ratios exceeding the sample mean and in counties with unconcentrated markets. We derive the latter from the Herfindahl-Hirschman index, which is based on country trends of firm sales by 3-digit North American Industry Classification System (NAICS) (i.e., HHI < 0.15). We assign a value of one to ventures located in areas with higher rates of startup activity and greater market competition (i.e., less market concentration and consolidation).
For political context, we construct two measures based on local and national voting patterns using the Dave Leip Atlas [114]. We identify Democratic counties based on the majority vote from recent national elections in alignment with the panel years at firm founding (Presidential and U.S. Senate). We also construct a binary indicator to identify alignment (or not) between local county voting patterns and national outcomes. Moreover, we include an indicator tracing state government technology-based economic development (TBED) policy activity to account for more localized government effects [53].
For government opportunity we construct two indicators based on venture proximity to U.S federal Procurement Technical Assistance Centers (PTAC) and Community Development Financial Institutions (CDFI), respectively. These organizations serve as government regulated training programs and account for access to publicly funded training opportunities [51,115]. Lastly, we identify the set of industries with disproportionate government contracting activity. Using comprehensive administrative records reported in the Federal Procurement Data System (FPDS), we identify the top decile of investments made to high-technology industries and the top decile of investments made to small businesses in 2015. For the latter, we leverage size rather than age as FPDS does not report details on establishment age; nevertheless, both age and size are correlated [116,117]. Together, this set defines the selection of industries most salient to the core research question around firm-government engagement for young high-tech ventures.
For internal factors of firm imprinting, we account for demographic features of the founder by identifying URM-owned – and, separately, woman- or minority-owned – firms. Additionally, we include several measures of growth. Using firm-level data on employment, we construct a binary indicator to distinguish larger from smaller ventures. Moreover, using firm-level records from Dun & Bradstreet (and subsequently reported in NETS), we include an indicator to identify whether the venture established any credit with a lender or supplier. We define both measures based on activity by firm age two. Separately, we identify the sample of ventures with any patenting activity by firm age three. For this measure, we match the population of ventures in the full sample to the corpus of assignees in USPTO (PatentsView) over the timeframe of interest (2015–2019). We use common text processing approaches in cleaning, standardizing, and matching firm name to define matches as those with greater than 0.95 similarity. Table 3 reports detail for the full set of variables, timing specification, sources, and functional forms.
Descriptive statistics
Table 4 reports descriptive statistics along with comparison of means and t-tests for the primary set of variables. We report descriptive statistics and comparison of means for raw measures in Table 5. In both tables, the comparison of means reports differences between the full sample of U.S. high-tech ventures to the subset that registered in SAM.
The most substantial observation is the consistent trend of differences in means. Comparing high-tech ventures that register in SAM to the full sample of U.S. high-tech ventures, the former set is substantially more likely to be URM-owned, smaller in size, and more likely to patent. In terms of external factors, ventures that register in SAM have greater access to institutional intermediaries and capital infrastructure, yet they are in areas with less entrepreneurial intensity. They are more likely to be in Democratic-leaning counties; however, local voting is less likely to be aligned with national political control. They are more proximate to PTACs and further from CDFIs. Lastly, while prevalent overall, they are slightly less likely to be in states with TBED policies, and they operate in industries that receive disproportionate support via government contracting.
S1 Table reports the results from the correlation matrix. Panel A reports the set of correlations for the primary variables (reported in Table 4), and Panel B reports the correlations for the measures with the raw functional form (reported in Table 5). Across all, correlations are limited, suggesting that multi-collinearity is not a concern [118].
Research design
Fundamentally, we aim to understand factors driving the choice made by firms to initiate and engage with the U.S. federal government. We set up a maximum likelihood logistic regression to assess the relationship of a series of external and internal antecedent factors on the dependent variable, Register in SAM. We express this relationship in Equation 1:
The data are structured in cross-sectional format, yet they account for dynamic activity over the initial three years of the firm’s operation. We do not execute a firm fixed effect model given that most regressors of interest are time-invariant. Specifically, the external and internal regressors are based on static measures (most at founding, though growth indicators are based on activity at firm age two or three), while the dependent variable captures activity by firm age three. In addition to the set of ecosystem indicators, we also include their interactions. Moreover, we include an exhaustive set of year, state, and industry dummies (i.e., 4-digit NAICS, representing industry group) based on firm founding details. Some indicators (i.e., FPDS, TBED) are perfectly co-linear with the set of industry and state fixed effects. Hence, in subsequent models, we directly assess these indicators with alternative specifications. For all, we estimate regressions with robust standard errors.
To reiterate, our core research question focuses on an endogenous process. We cannot discern causality. Rather, the validity of this design lies with sampling and model specification. To understand the antecedent factors that predict high-technology ventures to seek government engagement, it is necessary to account for the entire “at-risk” population; this comprises young (private) firms. Moreover, to parse apart whether and how various factors motivate the firm’s choice to engage with the government, it is necessary to trace an exhaustive list of external and internal factors. The data and computational demands to meet these specifications are significant, requiring extensive and precise detail. As previously discussed, we validate our source of sampling by comparing startup activity between NETS and the repository of Secretary of State databases. Moreover, we draw from proprietary (NETS and Pitchbook), administrative (SAM, FPDS, and NCSES), and public (SBA, FDIC, CDFI, David Leip Atlas, and State Science Technology Institute) resources. And we rely on a powerful server (32-core processors and 64 threads, 384 GB memory, and 32 TB storage) to construct the sample and run the analyses. We construct several measures in binary functional form for several reasons: (i) to allow for greater ease of interpretation of the regressors (i.e., estimating differential effects); and (ii) to optimize around the computational demands for a logistics model of this size and scale. Nevertheless, we examine incremental variation as well (i.e., S3, S4, and S5 Tables). All data, code, and log files are publicly available to replicate the empirical analysis (https://doi.org/10.7910/DVN/KT6136).
Results
Table 6 reports average marginal effects from the logistic regressions (Eq. 1). We report the results for internal and external factors, respectively in Columns 1 and 2, and the full specification in Column 3. We find evidence that both internal and external factors motivate firms to express formal interest in establishing a relationship with the government, though to varying degrees. The internal factors account for greater explanatory value than external factors; this is supported both by the size of the average marginal effects and differences in the adjusted r-squared values reported in Column 1 compared to Column 2.
Factors that increase the likelihood of firms to register in SAM include: URM-owned firms, smaller firms, those with credit, and those with patents. The coefficient for Size is negative. Larger firm size decreases the likelihood of firm-government engagement. Interpreted another way, smaller firm size increases the likelihood of firm-government engagement. As for external factors, entrepreneurial intensity reports the most prevalent negative effect, which suggests that greater entrepreneurial intensity substitutes for firm-government engagement. Though, the triple interaction accounting for all three ecosystem indicators is positive (albeit weakly significant and economically small). In the fully specified model, we report no effect for political context and a negative effect for CDFI access.
Probing the model further, we adjust the timing specification of the dependent variable by extending entry into SAM by firm age four and five. We report consistent results (S2 Table). Separately, we adjust the functional form of the regressors. First, rather than defining the ecosystem indicators based on median trends (refer to Table 3), we identify leading and lagging activity based on 25th and 75th percentile distributions of distance. We report the descriptive statistics in S3 Table (along with detail on the construction of the metrics) and regression results in S4 Table. We uncover interesting trends. Note, leading values (reported in Column 2 in S4 Table) define firms with closer distance to the external measures; lagging values (reported in Column 3 in S4 Table) define those with further distance. Ventures with especially close access to external resources report a complementary relationship with institutional intermediaries yet a substitutive relationship with entrepreneurial intensity. However, more limited access to capital infrastructure and entrepreneurial intensity (i.e., further access and distance) increases the likelihood for firms to engage with the government. Importantly, the diverging trends for entrepreneurial intensity reveal the same insight (i.e., offering “two sides of the same coin”). In short, entrepreneurial intensity serves as a substitute driver of firm-government engagement – when entrepreneurial intensity is accessible, ventures are less likely to seek government engagement, and when not accessible, they are more likely to do so.
Second, we estimate the primary regression with raw values of the regressors (refer to Table 5 for descriptive statistics of the raw measures). Generally, the results are consistent to main model specification, though the regression results reported in S5 Table report greater nuance for interpretation of marginal effects.
Heterogeneity analysis
We report heterogeneity analyses in Tables 7 and 8. The former examines heterogeneity based on internal factors, while the latter accounts for external factors. In Table 7, we split the sample by firm-ownership (i.e., non-URM, URM, woman, and minority-owned; Columns 2–5) and firm size (i.e., small vs. medium; Columns 6–7) and then re-estimate the primary specification across various sub-samples. For ease of reference, we re-report the primary results in Column 1.
Focusing first on the features of firm-ownership, we report some divergence and similarity of trends between non-URM and the latter set. Namely, the results for Any Credit report diverging trends between the two sets. In line with the primary results, non-URM owned firms report a positive, albeit economically small, effect for this measure of firm growth on the likelihood of initiating firm-government engagement, whereas we report a larger negative effect for URM and minority-owned firms. The latter set of results suggests a substitutive relationship between firm capability and firm-government engagement for historically under-served firms. Contrastingly, we report a positive effect for the measure of any patenting for non-URM owned firms and fail to report an effort for the latter set. Across all groups, we observe a similar negative effect for Size across all sample specifications with the largest magnitude reported for URM, woman, and minority-owned firms. In short, firm resource constraints appear to motivate such engagement regardless of ownership type. Additionally, we include two indicators – URM Venture Intensity and SAM Venture Intensity – to assess the extent to which greater concentration of local URM or SAM concentration by zip code affects such engagement. Most prominently, we report consistent positive results for SAM Venture Intensity.
Turning to features of firm size (reported in Columns 6 and 7 in Table 7), we bifurcate the sample by small (FTE < 5) and medium (FTE 6–10) sized business. Generally, we report consistent results across each sample specification. Though, we report some divergence for certain external factors. For medium-sized firms, access to capital infrastructure and, separately, location in a democratic leaning county (weakly) increase firm-engagement. The substitutive effects for entrepreneurial intensity and proximity to CDFI are pronounced for small firms.
In Table 8, we exploit geographic, industrial, and temporal features. (We are unable to include these in the primary model, Eq. 1, as they are perfectly co-linear with the set of dummies.) Namely, we split the sample by those states with TBED policies (Column 2), by industries with leading investments in federal contracting (FPDS, Column 3), and by time, distinguishing firms founded during the first Trump administration (Column 4). Again, Column 1 reports the primary results for ease of reference.
The results among the set of internal factors are consistent to the primary specification. We observe some divergence across the set of external factors. For example, high-tech ventures located in states with TBED policies or those that are founded during the first Trump administration report positive results for capital infrastructure. Whereas the substitutive effects of entrepreneurial intensity are prevalent for high-tech ventures located in states with TBED policies or those that operate in industries most salient to federal contracting priorities.
And as a final extension, we assess these external factors more directly in a separate model. Rather than estimating the primary model with a stratification technique (as reported in Table 8), we remove the set of state, industry, and year fixed effects to then include the indicators of FPDS, TBED, and Trump administration as regressors (again, without removing the fixed effects, we face the issue of perfect co-linearity). S6 Table reports these results, highlighting the complementary effect of FPDS and substitutive effect of the Trump administration. Regarding the former, high-tech ventures in industries that are more salient to federal contracting initiatives are more likely to engage with the government, while ventures founded during the first Trump administration are less likely to pursue such engagement. The remaining set of results are generally consistent to the primary model.
Discussion
This study seeks to investigate a key underlying and overlooked assumption in the literature – the firm’s strategic choice of whether or not to engage with the government. Prior scholarship most often references this assumption as they examine subsequent activity – namely, the estimation of innovative and commercial returns from various government programs [14,20–28]. We redirect attention to unpack this core prior assumption along specific observable but heretofore largely unexplored dimensions.
Generally, the results indicate that some factors complement the firm’s choice to engage with the government, while other factors counteract or substitute for such behavior. The most prominent complementary effects include: (i) firms with URM owners; (ii) small firms; and (iii) firms with greater early-stage growth potential (measured by credit and patent activity). Conversely, the substitutive effects most consistently include: (i) firms located in more intensive entrepreneurial settings; and (ii) firms located near community development financial institutions (CDFIs). Moreover, high-tech ventures operating in industries that are more salient to federal contracting initiatives are more likely to engage with the government, while ventures founded during the first Trump administration are less likely to pursue such engagement.
To place these results in context, it is important to consider the following. There is a significant gap for new firms between establishing the startup, developing the first prototype product or service, and securing the first significant revenues [119]. Often, a firm requires significant financial capital that is too risky for private investors. To fill this gap and correct private market capital inefficiencies, the government offers a variety of programs to support early-stage technology ventures. Government awards in the form of non-equity, non-dilutive R&D funds are designed to attract (crowd in) future private investment because they reduce scientific uncertainty and often serve as valuable validation for investors [120]. At the same time, government programs advance national interest missions (i.e., space, defense, public health, etc.) and seed robust national innovation ecosystems [4,51]. We report that firms facing resource constraints are more likely to seek such support as they navigate their early stages and overcome these constraints [45].
However, if we examine the results closely, the internal motivations to seek government support are not uniform across firms; this is especially apparent from the results reported in Table 7 (contrasting non-URM from URM-owned firms). From one vantage point, we report that new firms with growth aspirations – as indicated by reaching early-stage patent milestones and seeking to obtain financial credit – motivate the choice to engage with the government. Yet from another vantage point, we report that new firms owned by minoritized groups (i.e., URM-owned firms) seek such engagement. Notably, this set of firms reports contrasting trends around growth (and the results for patenting are inconclusive).
On the one hand, the former set of results suggest that young firms turn to the government to alleviate uncertainty in their R&D pursuits. Again, it is well documented that R&D demands are inherently risky and under-supported in the private sector [45,100]. In turn, our results capture a strategic choice whereby owners of young firms rely on the government in these early stages as they traverse the infamous “valleys of death” [38].
On the other hand, the results for the set of young firms owned by minoritized groups reveal a different set of motivations. A separate stream of literature highlights the detrimental role of institutional barriers placing owners from minoritized groups at a disadvantage [115,121,122]. While these owners also seek government engagement, their motivations critically differ, whereby the indicators of growth aspirations substitute for the choice to engage with the government. Importantly, firm owners across the population face different (and contrasting) constraints and motivations as they elect to engage with the government. These diverging results offer important implications for scholars, managers, and policymakers, which we explain below.
For scholars interested in examining the returns from government programs, it is essential to account for this variation in the research design. Scholars tend to rely on quasi-experimental designs to approximate causal estimates for the returns from government programs [14,20–28]. Our results offer new guidance around firm selection. Given that the motivations to seek government support are not uniform, it is critical that scholars account for this nuance and divergence as they construct plausible counterfactual samples and define appropriate model specifications.
That said, we emphasize that our analysis offers correlative, rather than causal, conclusions. Again, our research question addresses the core prior assumption of electing to engage with the government in the first place. In this study, we are focused on understanding an endogenous process. Contrastingly, the larger stream of scholarship to which our work offers contributions focuses on the subsequent impact of programmatic returns.
Usefully, our design begins with a population-level sample to assess antecedent factors that plausibly influence such firm behavior. We identify which firms choose (and do not choose) to engage with their government in the first place, and under which conditions they do so. By sampling from this population, our study offers guidance for appropriate public policy formulation and effective administrative oversight to stimulate and/or regulate market activity. Moreover, the results can inform outreach efforts to attract firms that can support governments’ policy objectives for their key stakeholders, including their constituents and citizenry.
More broadly, our research sheds light on the core question of what is the appropriate role of the government when engaging with high-tech ventures? If the primary policy objective is to address market failure [45], then the trends we uncover provide critical insight to guide proposed policy reforms. Based on our results, mapping antecedent factors to national technology needs and technology-readiness requirements could enable policymakers to adjust and revise government programs that create meaningful outcomes instead of legislating in the dark. Our research offers new insights and findings for evidence-based policymaking.
For firms’ managers and entrepreneurs, our research implications are two-fold. First, our findings imply that there may be a substantial untapped and largely overlooked opportunity for obtaining resources [123]. In the U.S., less than three percent of all high-tech ventures choose to engage with their government; and even a smaller share secure procurement [124]. Second, our findings further imply that given the overall small percentage of firms seeking such engagement, the competition for these resources from the government may be relatively low and reasonably obtainable. The federal government not only acts as input for technology ventures (in the form of grants, loans, etc.) but also as a customer (buyer) at scale through procurement [100]. This enables young firms to secure a sizable customer base and a steady revenue stream. At the same time, winning a government contract from the public market signals technical credibility of the firm and opens doors for potential commercial deals in the private market [6]. Understanding this system provides entrepreneurs and managers with actionable and repeatable playbooks to grow their firms and create a competitive advantage.
Limitations
Our study has limitations that future research may address. Of note, our sample focused on U.S. technology ventures within the 2015–2017 timeframe. We intentionally limited the sample specifications (i.e., sampling based on founding year, ownership, and industry orientation) to identify the most inclusive set of technology ventures at risk of such engagement. Certainly, the rate of government engagement would increase if we placed additional restrictions around how we define the at-risk sample (hence decreasing the sample size for the denominator). As the first large-scale nationwide exploratory analysis of the factors that drive ventures to engage with their governments, we elected to identify the “at-risk” set in this most inclusive manner. Future work could adjust (and perhaps further restrict this specification) if needed. In a similar vein, future research could extend this work by examining activity outside the U.S. and across broader time periods to help generalize the results.
In addition, the dependent variable captures the initial strategic move of firms to engage with the government. Within the U.S. context, once a firm registers in SAM, this is only the first of innumerable options to engage further with the government. As a follow up step in the process, firms must maintain their registration to continue access to federal programs. Moreover, firms can elect to engage more concertedly with the government, via participating in various government programs (i.e., SBIR/STTR) or securing government procurement. While we offer a baseline to understand initial government engagement, more work remains to unpack whether these antecedent factors consistently motivate decisions around deeper involvement with the government.
Supporting information
S3 Table. Additional descriptive statistics for ecosystem indicators.
https://doi.org/10.1371/journal.pone.0333710.s003
(DOCX)
S4 Table. Sensitivity analysis – ecosystem indicators extensions.
https://doi.org/10.1371/journal.pone.0333710.s004
(DOCX)
S6 Table. Extensions to model specification of fixed effects and regressors.
https://doi.org/10.1371/journal.pone.0333710.s006
(DOCX)
Acknowledgments
This research benefited from discussions with seminar participants at the Academy of Management, Portland State University, Arizona State University, DRUID, Southern Economics Association, Wake Forest University, and the Association for Public Policy and Management.
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