| Course | FIN 711 Financial Measures of Value Added (FIN/711) |
|---|---|
| Week | 8 |
| Paper type | Doctoral literature review and research proposal |
| Length | about 1,176 words, 4 double-spaced pages plus title page and references |
| Format | APA 7 student paper |
| School | University of Phoenix |
| Program | DBA |
| Updated | October 2026 |
Free sample paper for FIN 711 Week 8
What We Know and Need to Learn About Financing Ventures Like TerraSorb: A Review of Entrepreneurial Finance Research and a Proposed Study of Federal Grants and Later Valuations in Agricultural Technology
[Student Name]
University of Phoenix
FIN/711: Financial Measures of Value Added
Week 8 Assignment
[Instructor Name]
[Date]
TerraSorb is a composite used to frame the review; the research findings come from the sources listed, and the proposed study is a model design.
Over the course, TerraSorb, the composite Minneapolis soil carbon sensor company, served as a case for value measurement, financing stages, valuation, contracting, alternative financing and exits. One question recurred without a clear answer: did its federal research grant help it raise venture capital later, and at a better price? Practice raises questions that research has only partly answered, and the doctoral task is to find exactly where the answers stop. This paper reviews the research and proposes a study to extend it.
Stream One: Does Venture Capital Change Firms?
A large literature asks whether venture capital improves the firms it funds or merely selects firms that would succeed anyway. Hellmann and Puri (2002) documented that venture-backed startups professionalized faster, and Kerr et al. (2014) used a discontinuity in angel group voting to show that funding itself improved survival and growth. Da Rin et al. (2013) surveyed the field and concluded that both selection and value added matter, with the strongest causal evidence coming from settings where funding decisions involve near-arbitrary cutoffs. The stream has established that early investors can change outcomes, but much evidence still comes from correlations in samples dominated by software and biotechnology.
Stream Two: Contracting and Staging
A second stream studies how investors write contracts to manage information and incentive problems. Kaplan and Strömberg (2003) showed that real contracts separately allocate cash flow, control and liquidation rights in ways consistent with theory, and Gompers (1995) linked staging to uncertainty. Gornall and Strebulaev (2020) showed that the resulting preferred terms make headline valuations overstate fair value. This stream is strong on describing contracts and their pricing effects, but less able to show how contract terms causally affect firm outcomes, because terms are negotiated jointly with everything else about a deal.
Stream Three: Financing Beyond Venture Equity
A third stream examines debt, grants and other sources. Robb and Robinson (2014) showed that bank loans and other external borrowing fund a large share of new firms, more than outside equity in the early years. Hochberg et al. (2018) linked venture lending to patent collateral and investor commitment. Howell (2017) used the scoring cutoffs of Department of Energy research grants to show that early-stage awards roughly doubled the probability that a firm later raised venture capital, an unusually clean causal design. Grants, the evidence suggests, certify technology to later investors as well as funding it.
Stream Four: Financing Innovation
A fourth stream treats venture finance as a way of funding experiments. Kerr and Nanda (2015) and Lerner and Nanda (2020) argued that venture capital excels at funding projects that can be tested quickly and cheaply but struggles with capital-intensive, slow-to-prove technologies, such as energy and agriculture, where experiments take seasons or years. That argument implies that public funding may matter most precisely in sectors venture capital serves poorly.
What the Streams Leave Open
Taken together, the research establishes that grants can increase the probability of later venture funding, at least in energy, and that venture capital is less suited to slow-to-prove sectors. Three questions remain open. First, does a grant affect the price of later rounds, not just whether they occur, which matters for founders' dilution? Second, do the findings hold in agriculture, where testing depends on growing seasons and where the Department of Agriculture runs its own program? Third, does the certification effect operate through investors' information, through the technology's progress or both?
The Research Question
The proposed study asks: Do Department of Agriculture Small Business Innovation Research Phase II awards increase the pre-money valuation of recipients' subsequent venture rounds, and does any effect differ by the length of the technology's field-testing cycle?
Theoretical Grounding
The question rests on information asymmetry between founders and investors, which grants may reduce by providing expert certification, and on the experimentation view, which predicts larger effects where investors find it costly to test technologies themselves. If certification drives the effect, it should appear soon after the award and be larger for firms with less prior investor attention. If technical progress drives it, the effect should grow over the award period.
Design and Identification
The design borrows the approach of Howell (2017) and exploits the agency's proposal scoring. Applicants scored just above and just below the funding cutoff are likely similar in quality, so differences in their later financing can be attributed to the award. A regression discontinuity design would compare the valuation step-ups of firms just above and below the cutoff in subsequent rounds, controlling for round timing and market conditions.
Measures
The main outcome is the step-up in pre-money valuation between a firm's last round before the award decision and its first round after, adjusted for time elapsed and sector-wide valuation trends. Secondary outcomes include the probability of raising any round within three years, the size of that round and the share of the company sold. The length of the field-testing cycle, the moderator, would be coded from the proposal's description of the technology, for example one growing season for a sensor and several for a new seed trait.
Data
Award and proposal data would come from the program's public award records and, for scores, a data request to the agency, which may require a confidentiality agreement. Financing rounds and valuations would come from a commercial private-markets database, supplemented by state securities filings. Patent data would measure technical progress.
Threats to Validity
Scores may not be available or may be manipulated near the cutoff, which density tests can check. Valuation data for private rounds are incomplete and biased toward larger deals. Sample sizes near the cutoff in agriculture may be small, limiting statistical power. Results from one agency may not generalize to others.
Ethical and Practical Considerations
Proposal scores are confidential, so the study would need agency approval and secure handling of data, with results reported only in aggregate so that no applicant could be identified. Private valuation data come from commercial vendors whose licenses restrict sharing, which limits replication; the study would publish its code and describe the data so others with access could reproduce it.
Contribution
The study would extend causal evidence on grants from the probability of funding to the price of funding, test the certification and experimentation explanations against each other and inform both founders deciding whether to pursue grants and agencies designing programs for slow-to-prove sectors.
Return to the Case
For TerraSorb, the review suggests that its grant likely helped it raise its Series A and may have improved its terms, but research cannot yet say by how much. The proposed study would answer that question for companies like it.
Conclusion
Research on entrepreneurial finance has shown that early investors and grants can change venture outcomes, that contracts allocate control and value in predictable ways and that venture capital struggles with slow-to-prove technologies. A regression discontinuity study of agricultural research grants and later valuations would extend that knowledge where practice most needs it.
References
Da Rin, M., Hellmann, T., & Puri, M. (2013). A survey of venture capital research. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the economics of finance (Vol. 2A, pp. 573-648). Elsevier. https://doi.org/10.1016/B978-0-44-453594-8.00008-2
Gompers, P. A. (1995). Optimal investment, monitoring, and the staging of venture capital. The Journal of Finance, 50(5), 1461-1489. https://doi.org/10.1111/j.1540-6261.1995.tb05185.x
Gornall, W., & Strebulaev, I. A. (2020). Squaring venture capital valuations with reality. Journal of Financial Economics, 135(1), 120-143. https://doi.org/10.1016/j.jfineco.2018.04.015
Hellmann, T., & Puri, M. (2002). Venture capital and the professionalization of start-up firms: Empirical evidence. The Journal of Finance, 57(1), 169-197. https://doi.org/10.1111/1540-6261.00419
Hochberg, Y. V., Serrano, C. J., & Ziedonis, R. H. (2018). Patent collateral, investor commitment, and the market for venture lending. Journal of Financial Economics, 130(1), 74-94. https://doi.org/10.1016/j.jfineco.2018.06.003
Howell, S. T. (2017). Financing innovation: Evidence from R&D grants. American Economic Review, 107(4), 1136-1164. https://doi.org/10.1257/aer.20150808
Kaplan, S. N., & Strömberg, P. (2003). Financial contracting theory meets the real world: An empirical analysis of venture capital contracts. The Review of Economic Studies, 70(2), 281-315. https://doi.org/10.1111/1467-937X.00245
Kerr, W. R., Lerner, J., & Schoar, A. (2014). The consequences of entrepreneurial finance: Evidence from angel financings. The Review of Financial Studies, 27(1), 20-55. https://doi.org/10.1093/rfs/hhr098
Kerr, W. R., & Nanda, R. (2015). Financing innovation. Annual Review of Financial Economics, 7, 445-462. https://doi.org/10.1146/annurev-financial-111914-041825
Lerner, J., & Nanda, R. (2020). Venture capital's role in financing innovation: What we know and how much we still need to learn. Journal of Economic Perspectives, 34(3), 237-261. https://doi.org/10.1257/jep.34.3.237
Robb, A. M., & Robinson, D. T. (2014). The capital structure decisions of new firms. The Review of Financial Studies, 27(1), 153-179. https://doi.org/10.1093/rfs/hhs072
What the FIN 711 Week 8 instructions ask
The last FIN 711 assignment typically asks doctoral students to synthesize research on entrepreneurial finance and propose further study. Common requirements include a structured review of major research streams, evaluation of methods and evidence, identification of gaps or unresolved debates, a research question with theoretical grounding, a proposed design with data and identification strategy and a discussion of limitations and contributions to theory and practice. Many versions ask students to connect the review to a case or practical problem from the course. Organize the review by theme rather than by author, evaluate evidence critically, justify the gap and the design, and draw on primary sources cited in APA format.
How this FIN 711 Week 8 example is built
A practical question from the course, whether TerraSorb's federal grant helped it raise money later and at what price, opens a doctoral review of what research has established. The paper organizes the literature into four streams and summarizes the strongest findings in each, noting where identification is convincing and where it relies on correlation. The review shows that research has measured how grants affect the chance of later venture funding but says little about valuation step-ups or about sectors such as agriculture. A research question follows, with a design using grant scoring cutoffs, data from federal award records and private financing databases, the main threats to validity and the study's expected contribution.
FIN 711 Week 8 grading rubric: where the points go
Faculty grading this final doctoral paper usually reward a review organized by theme with critical evaluation of methods, a gap justified by the review and a feasible, well-identified design. Credit goes to papers that summarize findings accurately, distinguish causal evidence from correlation, show precisely where existing work stops and propose a study whose data and identification could answer the question. Discussing limitations and contributions to theory and practice demonstrates scholarly maturity. Tying the review back to the course's practical problem shows integration. Clear structure and primary research cited in APA style finish the paper. Faculty also value a frank account of what the proposed study could not show, since knowing a design's limits is part of doctoral judgment.
FIN 711 Week 8 help: mistakes to avoid
Doctoral literature reviews in FIN 711 Week 8 often summarize studies one by one without synthesizing them. Organize by theme and say what the stream as a whole has shown. Another frequent gap is a research question that the review does not justify; show that prior work leaves it open. Students also propose designs without saying how they would separate cause from correlation. Name the identification strategy. Avoid data sources you could not access. Discuss threats to validity honestly. Keep the question narrow enough to answer. Finally, explain who would use the findings and how, whether founders, investors or agencies designing grant programs.
Related FIN 711 sample papers
Other FIN 711 week samples
- FIN 711 Week 1: Value Creation and Financial Measures
- FIN 711 Week 2: Economic Value Added and Performance
- FIN 711 Week 3: Venture Financing Needs by Stage
- FIN 711 Week 4: Venture Valuation Methods
- FIN 711 Week 5: Angel and Venture Capital Investment
- FIN 711 Week 6: Debt and Alternative Financing
- FIN 711 Week 7: Private Equity and Exits
FIN 711 Week 8 questions, answered
What does FIN 711 Week 8 usually cover?
It usually covers synthesizing research on entrepreneurial finance, evaluating evidence and methods, identifying gaps and proposing a study with a research question, design, data and limitations.
Where can I find a free FIN 711 Week 8 sample paper?
A complete doctoral review of entrepreneurial finance research with a proposed study on grants and later valuations, with notes on each section, can be read here. Doctoral candidates can request a free first draft.
How should a doctoral literature review be organized?
By theme or research stream rather than by author, summarizing what each stream has established, how strong its evidence is and where questions remain open, leading to a justified research gap.
What is an identification strategy?
The approach a study uses to separate a causal effect from correlation, such as a natural experiment, a cutoff in a scoring rule or a comparison with a carefully matched control group.
What data are used in entrepreneurial finance research?
Common sources include private financing databases, government award and registration records, patent data, surveys of investors and founders and administrative records linked across sources.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official University of Phoenix document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
Request this one custom, free · All FIN 711 week samples · All courses