Venture Capital and Startup Agglomeration: Chen & Ewens (2025)

What this is. The paper’s core results, identification strategy, and estimating equations: enough to understand what it found and how, without reading all 46 pages. To replicate or extend, read the original at https://doi.org/10.1111/jofi.13451. Replication data are at https://github.com/michaelewens/Banks-In-VC.

TL;DR

The paper asks whether local VC supply drives the geographic concentration of high-growth startups in the United States. The identification lever is the Volcker Rule (implemented December 2013): banking entities were prohibited from investing in VC funds as limited partners (LPs), and this restriction fell disproportionately on Midwestern and Southern states where banks had historically been a larger share of VC fund capital (bank exposure up to 25% of LP capital in some states, below 5% in CA). Using a difference-in-differences design, the paper shows that states more exposed to the bank LP shock experienced: (1) fewer and smaller VC funds; (2) smaller startup financing rounds, lower pre-money valuations, and increased pre-VC financing; and (3) a 24-30% increase in startups migrating to VC hubs (CA, MA, NY), but no increase in migration to non-VC-hub states. The migration evidence directly links local VC supply to startup agglomeration.

Core results

Magnitudes and significance as reported; \*/\*\*/\*\*\* = 10%/5%/1%. “1-SD increase in bank exposure” corresponds to moving from a low-exposure state (e.g., New York) to a high-exposure state (e.g., Wisconsin or Missouri). Regression coefficients are multiplied by 100 in the migration tables (Table VIII). Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Fewer VC funds in high-exposure states after Volcker Rule	Table III Panel A col.(1), p. 2173	Bank Expo x Post = -0.036** (SE 0.013); 11% fewer VC funds per 1-SD bank exposure increase
R2	Less total VC capital raised in high-exposure states	Table III Panel A col.(5), p. 2173	Bank Expo x Post = -0.013*** (SE 0.006); 9% less total VC capital per 1-SD increase
R3	Smaller VC funds (intensive margin) in high-exposure states	Table III Panel B col.(1)-(2), p. 2174	Bank Expo x Post = -0.281* to -0.305**; 22% smaller fund size per 1-SD increase (NY to MO)
R4	Lower probability of raising a follow-on fund for pre-Volcker VC firms	Table III Panel C, p. 2174	Bank Expo = -0.030*** to -0.055**; by 2018, 10pp lower probability against mean of 51.4%
R5	Startups raise 7% smaller first VC rounds in high-exposure states	Table VI Panel A col.(1), p. 2180	Bank Expo x Post = -0.089*** (SE 0.027); 7% smaller first-round VC financing per 1-SD increase
R6	Startup pre-money valuations 9% lower (approx. $1.3M) in high-exposure states	Table VI Panel B col.(1), p. 2180	Bank Expo x Post = -0.112*** (SE 0.040); 9% lower pre-money valuation per 1-SD increase
R7	Startups 30% more likely to migrate to CA from high-exposure states post-Volcker	Table VIII Panel A col.(1), p. 2184	Bank Expo x Post = 0.117*** (SE 0.018); null for non-VC-hub states (placebo Panel C)
R8	24% more likely to migrate to any VC hub (CA, MA, NY) from high-exposure states	Table VIII Panel B col.(1), p. 2185-2186	Bank Expo x Post = 0.175*** (SE 0.041); differential vs. non-VC-hub confirmed significant

Overall (paper’s conclusion). The Volcker Rule created an unintended natural experiment that reveals a causal role for local VC supply in startup geographic clustering. The loss of bank LPs reduced VC fundraising and startup financing in non-hub regions; startups responded by migrating to VC hubs, exacerbating existing geographic disparities in entrepreneurship. VC funding constraints rather than industry alignment or geographic distance explain the migration direction. VCs outside traditional hubs are financially constrained, and nonlocal VCs cannot fill the gap because of information asymmetry and local bias in investing.

Theory / model

The paper has no formal structural model. The economic logic rests on two documented facts combined into a causal chain, with testable predictions:

Fact 1 (LP home bias). All LP types, including banks, exhibit significant in-state overweighting when investing in VC funds, following the finding of Hochberg and Rauh (2013) on LP home bias in private equity. Table V Panel A (p. 2178) shows that bank LPs allocate 23.2% of investments to in-state VCs, which is 11.5% above the share of all VC investments in the state (benchmark BM1) and 11.9% above the share of all out-of-state investments in the state (BM2). This surpasses all other LP types (pension funds: 20.8%; endowments: 12.6%).

Fact 2 (geographic heterogeneity in bank LP exposure). States outside the major VC hubs had historically higher bank LP shares, consistent with the geographic VC concentration documented by Chen et al. (2010). The Midwest and South had bank exposure ratios (bank-years with VC revenue per VC fund raised) of 2.44 and 2.11 respectively, versus 0.84 for the Northeast and 0.57 for the West (Figure 2 Panel A, p. 2168; Table I, p. 2166).

Causal chain. If bank LPs are locally biased and the Volcker Rule removes bank LPs disproportionately from non-hub states, then: (1) local VC supply falls more in those states; (2) startups there receive less and cheaper-priced capital; (3) startups facing local VC shortfalls migrate to where VC is available. The approach is analogous to Kortum and Lerner (2000), who assess VC contributions to innovation using supply-side variation, and Gonzalez-Uribe (2020), who uses LP supply shocks to trace VC effects. The migration channel resolves the ex ante ambiguity: VCs could conceivably substitute distant LPs, or nonlocal VCs could fill the gap. The results rule both out, attributing the failure to information asymmetry (a 10% increase in distance between an out-of-state VC and a startup is associated with a 0.5% higher likelihood of requiring a local co-investor; Table XII Panel A, p. 2193). The changes in startup valuations mirror findings in Gompers and Lerner (2000), who show that VC inflows create demand pressure and drive valuation changes.

The paper extends Samila and Sorenson (2011), who instrument VC supply with endowment LP returns at the MSA level; this paper adds a cleaner regional shock via the Volcker Rule.

Identification assumption. Parallel trends: high- and low-bank-exposure states evolved similarly in pre-Volcker VC activity (2010-2012). The dynamic estimation in Table IV (p. 2176) confirms pre-trend coefficients for Bank Expo x 2010, 2011, 2012 are all insignificant across outcome variables, with treatment effects emerging only from 2014 onward.

Method

The primary estimator is a difference-in-differences regression (equation 1, p. 2170). The unit of analysis varies by outcome: state-year (VC fundraising aggregates), VC-fund level (fund size and follow-on), or startup level (financing and migration). The baseline specification is:

$$Y_{it} = \beta_1 \, \text{Bank Expo}_i \times \text{Post}_t + \beta_2 X_i + \gamma_t + \epsilon_{it} \tag{1}$$

where $\text{Bank Expo}_i$ is the state-level treatment variable measuring VCs’ pre-Volcker reliance on banks as LPs (the ratio of aggregate bank-years with VC revenue to the number of VC funds raised in the state over 2001-2013); $\text{Post}_t = 1$ for 2014-2018 (the post-Volcker period); $X_i$ is a vector of entrepreneurial firm characteristics, state fixed effects, founding year fixed effects, and industry fixed effects; and $\gamma_t$ is year fixed effects. The main coefficient of interest is $\beta_1$.

The treatment variable is continuous, capturing cross-state variation richer than a binary High/Low split. It builds on difference-in-differences and panel-regression. Identification relies on the differential shock to LP capital across states: the Volcker Rule’s scope for VC funds was unexpected (Congress did not intend to include VC funds), so pre-2014 bank LP distribution was not strategically adjusted in anticipation.

The paper also runs a within-VC-firm analysis (Panel C of Table III): a single-difference regression of whether a pre-Volcker VC firm raised a follow-on fund by year $t \in \{2014, ..., 2018\}$ on Bank Expo, controlling for the last pre-Volcker fund vintage year fixed effects.

Empirical specifications

VC fundraising (R1-R4). State-year DiD with state and year fixed effects (Table III Panel A). Dependent variables: ln(1 + number of VC funds), ln(1 + total VC capital raised). State-year-level controls follow Gompers and Lerner (1998): GDP growth, log GDP per capita, house price growth, STEM employment growth. Errors clustered by state. Robustness: exclude California, narrow to 2011-2017, Poisson regression, IHS transformation.

VC fund size (R3). VC-fund-level DiD with VC firm fixed effects, vintage year fixed effects, and fund-sequence fixed effects (Table III Panel B). Dependent variable: ln(VC fund size). Errors clustered by state. Sample: 1,617 VC funds, 2010-2018.

Startup financing (R5-R6). Startup-year DiD with HQ state fixed effects, financing year fixed effects, founding year fixed effects, Series A or Seed fixed effects, and industry fixed effects (Table VI). Dependent variables: ln(capital raised in first VC round), ln(pre-money valuation), equity sold, ln(syndication size), indicator for pre-VC financing. Sample: 11,048 startups (5,903 for valuation). Errors clustered by startup HQ state.

Startup migration (R7-R8). Startup-year DiD at startup level (Table VIII). Dependent variables: dummy for moving HQ to CA (Panel A), to VC hubs CA/MA/NY (Panel B), or to non-VC-hub states (Panel C, placebo). Fixed effects: incorporation state, origin state, year, founding year, industry. Errors clustered by startup initial HQ state. Sample: 56,487 startup-year observations. All regression coefficients multiplied by 100.

$$Y_{ist} = \beta_1 \, \text{Bank Expo}_s \times \text{Post}_t + \beta_2 X_{is} + \text{FE} + \epsilon_{ist}$$

The triple-difference specifications (Tables IX-XI) add a third interaction (industry alignment, geographic distance, or VC financing constraints) to explore heterogeneity in migration responses. The geographic distance specification (Table X) finds distance does not explain migration choices post-shock; the VC-funding-constraint specifications (Table XI, Panels A-C) show biotech startups, older startups, and startups in high-VC-funded industries are more likely to migrate.

Datasets used

Dataset	Role in paper	Wiki page
Call Reports (FFIEC) + FR Y-9Cs (BHC filings)	Construct bank VC revenue series 2001-2013; identify banking entities investing in VC; build treatment variable Bank Expo	[no page yet]
VentureSource (CB Insights / Dow Jones)	VC fund characteristics, startup financing rounds, startup HQ state over time; primary VC data 2010-2018	[no page yet]
Pitchbook	VC fund and startup data (robustness checks); LP commitment information	PitchBook (licensed)
SEC EDGAR (Form D filings)	Identify startup migration via consecutive business-address changes 2002-2018; 56,487+ startup-year observations	EDGAR
Preqin	LP commitment data for robustness checks (LP home bias analysis, Table V)	Preqin (licensed)

Sample: state-year panel covers 35 U.S. states, 2010-2018 (315 state-year observations). VC fund sample: 1,617 funds. Startup sample: 11,048 startups (first Seed or Series A round, $100M); 1,700 identified as having ever moved to a different state.

When to read the full paper

Read the original if you are: studying causal drivers of startup geographic concentration; evaluating the regional impact of financial regulation (Dodd-Frank, Volcker Rule); interested in how LP capital constraints propagate through the VC intermediation chain to real activity; or building on the startup migration literature. The Internet Appendix contains additional robustness tests (Tables IA.I-IA.XIV), including placebo tests on banking sector outcomes, alternative treatment variable constructions, and the LP home-bias theoretical exercise. Replication data and code are at https://github.com/michaelewens/Banks-In-VC.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(4), August 2025. This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. The paper is paywalled (Wiley VOR terms, not CC-licensed); only core results are extracted here.

Chen, Jun, and Michael Ewens. “Venture Capital and Startup Agglomeration.” The Journal of Finance 80, no. 4 (August 2025): 2153-2198. DOI: 10.1111/jofi.13451. © 2025 the American Finance Association. Extract-only: the Wiley VOR licence does not permit redistribution.