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REVIEW 2 major objections 4 minor 54 references

Geography in Online Capital Allocation: Evidence from Equity-Based Crowdfunding

T0 review · 2 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Same-prefecture investors 6.7 points likelier to fund regional deals

desk verdict A careful stage-level decomposition of local bias in Japanese equity crowdfunding, with a credible same-prefecture post-view premium that survives a connected-investor screen, though the causal interpretation is constrained by selection into page views. read the letter →

arxiv 2607.29162 v1 pith:LIZ2OTBS submitted 2026-07-31 econ.GN q-fin.EC

Keiichi Kawai , Akira Matsushita This is my paper · ORCID
classification econ.GNq-fin.EC
keywords geographyequitycrowdfundinglocalbiashomeJapaninvestorbehaviorfixedeffectsFundinno
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether online access makes investment geography a thing of the past. Using user-campaign records from Japan's dominant equity-crowdfunding platform, Fundinno, it traces each user from listing exposure to campaign-page view to investment, and compares same-prefecture, adjacent-prefecture, and other-prefecture users. The central finding: for campaigns outside the Tokyo metropolitan area, viewers from the issuer's own prefecture are 6.7 percentage points more likely to invest after viewing the campaign page, relative to the roughly 10 percent investment rate among other-prefecture viewers. The adjacent-prefecture premium is only 0.9 points, and the same-prefecture premium is small for Tokyo-area campaigns. This shows that the local premium is not only a discovery effect: it persists after users have opened the campaign's detailed information page.

What carries the argument

The identifying device is the two-stage user-campaign funnel recorded by the platform: listing exposure -> campaign detail-page view -> investment. Using two-way fixed effects (user and campaign), the paper examines two conditional margins — listing-to-view (likely discovery) and view-to-investment (post-access demand). The key comparison is the same-prefecture coefficient in the view-to-investment regression for non-TMA campaigns, benchmarked against adjacent-prefecture and TMA coefficients, so the same-prefecture locality claim rests on the contrast across those groups.

What would settle it

Compute the view-to-investment premium for non-TMA campaigns separately for users who reached the campaign page directly from an external link (bypassing the listing page). If the 6.7-point same-prefecture premium collapsed to zero in that direct-access subsample, the claim that the premium represents post-access demand rather than residual discovery or attention would be falsified.

Watch

Extended reading notes

Core claim

The paper establishes that a same-prefecture premium in equity-crowdfunding investment survives conditioning on campaign-page access. For non-TMA campaigns, same-prefecture viewers have a 6.7 percentage-point higher probability of positive net investment relative to other-prefecture viewers, against a base rate near 10 percent. The adjacent-prefecture difference is only 0.9 points and the same-prefecture difference for TMA campaigns is 0.6 points, so the premium is concentrated in same-prefecture pairs outside the Tokyo metropolitan area. Because the premium appears after users view the detailed campaign page, it cannot be explained by discovery alone; the small adjacent-prefecture gap also

Load-bearing premise

The interpretation hinges on the claim that the view-to-investment margin isolates demand generated after the page view, and not pre-existing same-prefecture connections (issuer outreach, prior ties) that survive the connected-investor screen.

Editorial extensions

If this is right

  • If the premium truly operates after campaign-page access, online platforms can widen reach without making capital allocation geographically neutral.
  • Regional (non-TMA) issuers may continue to rely on same-prefecture households even when listed on a national platform.
  • The modest adjacent-prefecture gap implies frictions or preferences are bound to prefectural boundaries, not to continuous distance, at least at this level of aggregation.
  • The small TMA same-prefecture premium suggests that 'same prefecture' captures distinct local context outside the metropolitan core, not simply the administrative boundary.
  • If the underlying motive were identified, the result could speak to whether the platform's local information environment or purely nonpecuniary home-support drives the effect.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A design experiment that randomizes the display of the issuer's prefecture on the listing page could isolate whether the viewing-margin premium is a cue effect; the paper's display evidence is descriptive because display is not assigned.
  • Applying the same funnel decomposition to other online finance platforms (green bonds, real estate crowdfunding) would test whether the post-access home premium is specific to equity or general to digital capital allocation.
  • The authors remain agnostic about motive; if the premium is nonpecuniary home support, then even perfect information and friction-free platforms would not flatten the geographic pattern, whereas an information channel would respond to enhanced disclosure.
  • For policymakers, the results imply that promoting online equity finance in rural areas may not by itself reduce reliance on local capital; supplementary policies might target outside investors or inter-regional networks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper uses proprietary two-stage (listing exposure, detail-page view, investment) user-campaign records from Fundinno, Japan's dominant equity-based crowdfunding platform, to decompose the geography of online investment into a discovery margin (listing-to-view) and a post-access margin (view-to-investment). With user and campaign fixed effects on 527 campaigns, it finds that for campaigns outside the Tokyo metropolitan area (non-TMA), same-prefecture viewers are 6.7 percentage points more likely to make a positive net investment after viewing the campaign detail page, relative to other-prefecture viewers whose rate is about 10 percent. The adjacent-prefecture difference is only 0.9 percentage points, and the same-prefecture premium for TMA campaigns is 0.6 percentage points. Logit average marginal effects are reported as nonlinear checks; for the non-TMA investment margin the logit AME is 9.2 percentage points. A robustness exercise excluding narrowly-defined likely connected investors lowers the non-TMA same-prefecture estimate to 5.1 percentage points. The paper interprets the pattern as evidence that the local premium is not purely a discovery effect and is not a smooth proximity gradient, while explicitly acknowledging that the data do not identify the underlying motive.

Significance. If the central result holds, the paper makes a meaningful contribution to the literature on geography in online financial markets. Its strengths are the unusually rich platform data linking exposure, viewing, investment, and geography for nearly the entire Japanese ECF market; the clean separation of two conditional margins; the use of two-way fixed effects with two-way clustered standard errors; the inclusion of logit checks; and the transparent support tables in the appendix. The paper is appropriately cautious in labeling its estimates as descriptive and in stating that prior ties, issuer outreach, and home-prefecture support motives remain possible channels. The main limitation is that the post-access interpretation relies on the view-to-investment contrast being informative about investment demand after access, rather than about selection into viewing; the paper does not exploit the preview/investment phase timing to address this directly.

major comments (2)
  1. [§5.1, Table 6; §2.3] The view-to-investment coefficient theta^I,same_non-TMA = 0.067 is conditional on a recorded detail-page view. Because same-prefecture status strongly affects the earlier margin (Table 6, listing-to-view: 0.027, with an additional 0.093 when geographic information is displayed), the investment-margin contrast is identified on a selected sample. The paper acknowledges this in §4.3, but the preview/investment phase described in §2.3 provides a feasible test: report specifications that split by whether the user's first detail-page view occurred during the preview phase (when investment is impossible) or during the investment phase. If the 6.7pp premium is present among preview-phase first viewers, the interpretation that the premium appears 'after campaign-page access' is strengthened; if the premium is absent there, the result may largely reflect same-prefecture users viewing once they are
  2. [§5.4, Table 8] The connected-investor exclusion removes only users who registered within 21 days, invested in one campaign, and viewed fewer than five pages. This screen does not remove more common prior ties or direct issuer outreach, which are plausibly correlated with same-prefecture status. The estimate falls from 0.067 to 0.051, but the residual could still be driven by such ties. I recommend either stronger screens (e.g., excluding users with any history of same-prefecture page views or investments, or using registration timing relative to campaign start) or a formal sensitivity/bounds analysis, and a more cautious statement that issuer outreach/prior ties are not the primary driver.
minor comments (4)
  1. [Table 6] The logit AME for non-TMA same-prefecture view-to-investment is 0.092 versus the LPM 0.067. This is a non-trivial gap; please discuss whether it reflects attrition in the fixed-effect logit sample, nonlinearity, or differences in the averaging method, and present the main magnitude as a range if appropriate.
  2. [§3.4] Please define 'qualifying listing-page exposure' and 'first observed detail-page view' precisely, including how ties or orderings between exposure and view are handled when constructing the samples.
  3. [§4.5] The statement that LPM coefficients are 'directly interpretable as percentage-point differences' is fine, but the computation of the logit average marginal effects (the baseline over which averaging is performed) should be documented in the table notes or text.
  4. [References] The two Japan Securities Dealers Association (2026) entries are both cited as 2026; please distinguish them by adding months or report numbers, as in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central premium is an estimated conditional contrast, not an input or a self-citation chain.

full rationale

The paper's central quantities are estimated from data. The same-prefecture view-to-investment premium (Table 6, Eq. 2: non-TMA same-prefecture coefficient 0.067) is a regression coefficient from a two-way fixed-effect linear probability model on user-campaign records; it is not defined in terms of the claim it supports. The outcome `inv` is defined as positive net investment with first detail-page view no later than first investment, which is a measurement condition on all investors rather than a restatement of the geographic contrast. The viewing margin (Eq. 1) and investment margin (Eq. 2) are separate conditional probabilities, and the paper explicitly treats the fixed effects and sample restrictions as imperfect controls for pair-specific channels rather than as structural identification. The only self-reference in the paper is the footnote that an earlier version circulated under a different title; this is not cited as evidence and is not load-bearing. No uniqueness theorem, ansatz smuggled in via citation, or fitted parameter renamed as a prediction appears. The paper also explicitly disclaims identifying the motive ('The data do not identify the underlying motive'), so the descriptive label 'same-prefecture locality' is an interpretation of an estimated pattern rather than a definitional input. I can quote no equation or passage that reduces the claimed premium to its own construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No ad hoc fitted free parameters are used; all coefficients are estimated and reported with standard errors. The paper introduces no new entities or mechanisms. Its core assumptions are data accuracy, log integrity, and limited unobserved pair-specific confounding.

assumptions (4)
  • domain assumption The platform access logs correctly record listing exposure, detail-page views, and the temporal order of views and investments.
    Central to separating pre-access attention from post-access investment. If logs are incomplete or the ordering rule in Table 4 is wrong, the view-to-investment margin is mismeasured.
  • domain assumption User residence prefecture and issuer headquarters prefecture in the data are accurate and stable.
    Same-prefecture and adjacent-prefecture classifications depend on these fields.
  • domain assumption After conditioning on user and campaign fixed effects, local and adjacent indicators are not confounded by unobserved pair-specific shocks such as direct issuer outreach or prior ties.
    The paper acknowledges this in Sections 4.4 and 5.4. If such shocks correlate with same-prefecture status beyond the stress-test screen, the estimated premium could overstate geography-driven demand.
  • standard math There is sufficient within-user and within-campaign overlap to identify the fixed-effect coefficients.
    Appendix C documents overlap; without it the two-way fixed-effects specifications are not identified.

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Cite this review

Pith. "Pith review of Geography in Online Capital Allocation: Evidence from Equity-Based Crowdfunding." pith.science (2026). https://pith.science/paper/LIZ2OTBS

@misc{pith2026260729162,
  author       = {Pith},
  title        = {Pith review of: Geography in Online Capital Allocation: Evidence from Equity-Based Crowdfunding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LIZ2OTBS}},
  note         = {Machine review of arXiv:2607.29162}
}
read the original abstract

Digital investment platforms reduce search costs, yet realized investments can remain geographically concentrated. Such concentration alone cannot distinguish whether local investment reflects initial salience, proximity-related information, prior ties, or nonpecuniary motives such as support for firms in one's home prefecture. We use user-campaign records from Fundinno, Japan's dominant equity-based crowdfunding platform, which link listing exposure, campaign-page views, and investments to user residence and issuer location. For campaigns outside the Tokyo metropolitan area (non-TMA), same-prefecture users are 6.7 percentage points more likely to invest after viewing a campaign page, relative to an other-prefecture investment rate of roughly 10 percent. The corresponding adjacent-prefecture difference is only 0.9 percentage points, and the same-prefecture premium is small for campaigns inside the Tokyo metropolitan area. The premium therefore remains after observed campaign-page access, is much larger than the adjacent-prefecture difference, and is concentrated outside the metropolitan core. These findings show that online access does not make investment demand geographically neutral: for regional issuers, same-prefecture investors remain disproportionately important even after campaign-page access. The data do not identify the underlying motive, but the pattern is difficult to explain by discovery alone or by smooth proximity.

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Reviewed August 3, 2026 · model on record in the stance chip above.