When the trustee for Bernard L. Madoff Investment Securities LLC ("BLMIS") filed the customer list with the bankruptcy court in early 2009, the document read less like a financial register than a parish directory. Page after page of addresses clustered in the same handful of zip codes — the Upper East Side, Great Neck, Boca Raton, Palm Beach — and then dispersed outward in surprising eddies through Minneapolis, suburban Atlanta, and the Pacific Northwest. To a casual reader the geography looked random. To Emily Greene Owens and Michael Shores, two Cornell economists trained to suspect that randomness conceals structure, the list looked like a dataset waiting to be regressed against something. The something turned out to be the location of America's Jewish congregations.
The previous chapter took up the qualitative side of the same question: Stolowy and her co-authors describe the rhetorical machinery by which a "trustworthy investment opportunity" is constructed (see [stolowy-trustworthy-investment-opportunity]). Owens and Shores supply the map. Where Stolowy et al. theorize the trust-producing networks, Owens and Shores show, county by county, where those networks were thickest and where, in consequence, the approximately $64.8 billion in paper account balances reported on the November 2008 BLMIS statements (a figure parsed in detail in [second-circuit-net-equity-madoff]) found their human substrate.
Editorial note on the subject matter
Before turning to the regression, a clarifying word. Bernard L. Madoff is the most expensive single instance of affinity fraud in American history, but he is an instance, not the genus. The Commission's investor-education materials catalogue affinity schemes targeted at Mormon families in the Mountain West, Cambodian and Vietnamese immigrants in California, Haitian congregations in South Florida, Korean-American small-business owners, evangelical churches across the Sun Belt, and AAPI investment clubs in the Pacific Northwest. See SEC Office of Investor Education and Advocacy, Affinity Fraud: How to Avoid Investment Scams That Target Groups, Investor Alert (rev. ed. 2013). Owens and Shores study Jewish networks because the Madoff customer list happens to be the largest and best-documented dataset of its kind; the mechanism they identify is general. To read their paper as a story about Jewish credulity rather than about the structural vulnerability of any tight-knit community to one of its own is to misread it.
What the paper actually does
The empirical strategy is disarmingly clean. The unit of analysis is the U.S. county. The dependent variable is the count, and the per-capita rate, of Madoff victims in that county, drawn from the customer list released through the SIPA liquidation overseen by Irving H. Picard, the SIPA trustee. The principal independent variable — the density of Jewish institutional networks — is measured two ways. The first measure draws on the Religious Congregations and Membership Study (RCMS), a decennial county-by-county census of religious bodies maintained by the Association of Statisticians of American Religious Bodies; the variable is the share of religious adherents in the county who are Jewish. The second measure draws on IRS Form 990 filings — the annual information return that tax-exempt organizations must file under §6033 of the Internal Revenue Code — aggregated by the National Center for Charitable Statistics; the variable is the revenue of Jewish non-profit organizations relative to other religious non-profits.
The controls are the standard kit — county income, wealth indicators, education, age structure, urbanization — plus two distance measures: highway miles from Manhattan and from Palm Beach, the two physical hubs of Madoff's social life. The intuition behind the controls is that rich, educated, coastal counties produce more sophisticated investors of every stripe; the question is whether Jewish density adds explanatory power after wealth and proximity are absorbed.
It does. Counties one standard deviation above the mean in Jewish-network density produced substantially more Madoff victims per capita than otherwise similar counties, and the coefficient survives every plausible specification. Distance from New York and Palm Beach has the expected protective effect — victimization falls with miles — but the interaction term tells the real story. In counties with thick Jewish institutional networks, distance stops mattering. A congregation in Denver behaves, for purposes of exposure to Madoff, more like a congregation in Roslyn than like its gentile neighbors three blocks away. The network, in other words, is a geographic shortcut: it collapses the thousands of miles between New York and the Mountain West into the social distance between two board members who served together on a federation campaign.
Why a law student should care
Affinity fraud is not a statutory term. It appears nowhere in the Securities Act of 1933, the Securities Exchange Act of 1934, or the Investment Advisers Act of 1940. It is an enforcement category — a label the SEC's Office of Investor Education and Advocacy has used since the late 1990s to describe schemes that exploit pre-existing trust networks. The doctrinal home of an affinity-fraud case is still Rule 10b-5, 17 C.F.R. § 240.10b-5, or §206 of the Advisers Act, 15 U.S.C. § 80b-6, or the mail and wire fraud statutes; affinity simply describes the marketing channel.
That doctrinal thinness has consequences. Because affinity is not an element of any cause of action, courts have rarely been asked to define it, and the empirical claim underlying the category — that shared identity actually does predict victimization, holding wealth and access constant — had gone largely untested. Owens and Shores supply the missing test. For a student working on §10(b) reliance, on the fraud-on-the-market presumption refined in Halliburton Co. v. Erica P. John Fund, Inc., 573 U.S. 258 (2014), or on the scienter standards of Tellabs, Inc. v. Makor Issues & Rights, Ltd., 551 U.S. 308 (2007), the paper offers an empirical foundation for an argument that has rested on intuition: that homogeneous communities are unusually credulous because they substitute social vouching for the arms-length skepticism securities law presumes investors will exercise.
The non-profit comparison
The most legally interesting finding sits in a table near the back of the paper. Madoff's victim list includes hundreds of non-profit organizations — synagogues, foundations, university endowments, hospital trusts — alongside individual investors. Owens and Shores run their specification separately on each group and find that the affinity coefficient is meaningfully weaker for non-profits. Charitable endowments in high-density Jewish counties were more exposed than those in low-density counties, but the gap was smaller, and a substantial share of the effect ran through the personal networks of board members rather than through institutional channels.
The authors interpret this gap as evidence that formal due diligence partially substitutes for the credulity affinity produces. Non-profits, even small ones, tend to retain investment consultants, hold trustee meetings, and document an investment policy statement — the prudent-investor scaffolding required by the Uniform Prudent Management of Institutional Funds Act (UPMIFA) and analogous state law. Those processes do not eliminate the affinity effect, but they discipline it. Individual investors, who owe no fiduciary duties to themselves, absorb the full force of the network.
The strength of informal networks appears to substitute for the formal due diligence that securities regulation presumes investors will perform.
Implications for enforcement and doctrine
The findings cut against several pieties of securities regulation. The reliance element of Rule 10b-5 assumes a discrete investor weighing disclosures; the Owens and Shores data suggest that for affinity victims the operative reliance is on the network — the synagogue board member who introduced the broker, the foundation president who was already in — and not on any document Madoff produced or failed to produce. The clawback litigation against net winners — investors who withdrew more than their principal before the collapse — takes on a different ethical valence once one accepts that early investors in affinity schemes are typically the same people who, by vouching, brought the later victims in. If Owens and Shores show where the victims clustered, Gurun, Stoffman, and Yonker show how the trust they shared evaporated across regions in the aftermath (see [gurun-stoffman-yonker-trust-busting]).
A natural extension would marry the county-level network measure to the dollar-weighted claims data later released by the trustee, asking whether affinity predicts not just whether one is in, but how deep. A second open question is mechanism: the paper identifies the network effect but cannot distinguish among the channels through which it operates. Is it that congregants hear about Madoff from each other? That clergy and lay leaders introduced congregants to him? That the imprimatur of Jewish charities investing with Madoff served as a Good Housekeeping seal? The data are consistent with all three.
Doctrinal takeaway
For the law student, Owens and Shores supply the empirical predicate for three doctrinal threads. First, the Commission's affinity-fraud enforcement priorities — articulated in successive OIEA alerts and now an established programmatic focus — gain analytic respectability from the paper's demonstration that "affinity" describes a measurable causal channel and not merely a marketing pattern. Second, in advisory-fraud actions under §206 of the Advisers Act, the "common course of dealing" inquiry that courts use to bundle individual victims into a single scheme maps onto the network density Owens and Shores quantify: the network is the common course. Third, the paper poses a policy question the doctrine cannot answer on its own — how a Commission that depends on investor complaints, broker disclosures, and arms-length skepticism is to reach communities that vouch for one another precisely because they distrust outside regulators. The empirical map of trust is also a map of the Commission's blind spots.