Going for Broke: de Jong, Kooijmans & Koudijs (2025)

What this is. The paper’s core results, the stylized model motivating the empirical analysis, and the estimating specifications with their equations: enough to know what was found and how, without reading all 50 pages. To replicate or extend, read the full source at the original.

TL;DR

The paper asks whether bank reputation can improve security quality in opaque markets. It studies 37 Amsterdam merchant banks that securitized West Indian plantation mortgages between 1753 and 1772, the first large-scale mortgage-backed securities market on record. High-reputation banks (measured by the rental value of their office premises, capturing outside business at stake) originated better-quality mortgages and issued securities that retained on average 17.5 percentage points more value during the market collapse. Virtually all of this premium can be traced to better mortgage characteristics at origination, not ex-post behavior. The reputation effect is significantly attenuated for bankers who were married into wealth (shielded from downside risk) or who had a short-run profit focus (partner died with a minor heir). These findings are consistent with a partial-equilibrium model predicting that reputation disciplines behavior only when bankers are personally exposed to long-run reputational losses.

The results contrast sharply with evidence from modern securitization markets. Griffin, Lowery, and Saretto (2014) show that high-reputation banks continued issuing large volumes of poorly performing MBS in the 2000s. Piskorski, Seru, and Witkin (2015) find that similar misrepresentation rates prevailed regardless of bank reputation in modern RMBS. The paper argues these modern failures reflect structural differences: limited liability, bailout expectations, and short-term incentives removed the personal downside exposure that makes reputation effective. On the theoretical side, Winton and Yerramilli (2021) model reputation as a disciplining device in originate-to-distribute lending, providing the framework this paper empirically supports. Hartman-Glaser (2017) shows how reputation can instead lead to pooling equilibria where opportunistic types mimic good types, a channel this paper finds limited evidence for in the historical setting. The companion data paper, de Jong, Kooijmans, and Koudijs (2023), documents the plantation MBS data set and provides evidence on the full intermediation chain. Flandreau and Flores (2009) document a related reputation-quality link in 19th-century sovereign bond markets.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%.

#	Result	Locator	Magnitude
R1	High-reputation banks originated mortgages with lower LTVs during the boom	Table II, p. 3293	LTV boom: high-rep ~0.56 vs low-rep 0.62 (SD 0.09); difference -0.06*** (t = -2.67)
R2	High-reputation banks used elite agents and borrowers more during the boom	Table II, p. 3293	Nonelite agent boom diff: -0.22** (t = -2.08); nonelite borrower boom diff: -0.35*** (t = -3.58)
R3	MBS issued by high-reputation banks retained 17.5 percentage points more value in the bust	Table III col. (1), p. 3295	High-reputation dummy coeff: 17.46*** (t = 5.10); R² = 0.72; N = 4,605 transactions, 46 MBS
R4	The reputation-price gap is robust to all alternative reputation measures	Table III cols. (2)-(9), p. 3295	Continuous office value: 24.25*** (t = 5.14); city government: 14.17*** (t = 5.85); log ABE vol: 1.63* (t = 0.85); log N deeds: 2.10 (t = 2.03)
R5	Mortgage characteristics at origination mediate 73% of the reputation-price gap	Table IV Panel B, p. 3300	Joint ACME: 10.29 (p = 0.10); remaining direct effect after all mediators becomes statistically insignificant
R6	The reputation-price gap is small (about 6 pp) during the boom and large (about 35 pp) during the bust	Figure 5 Panel A, p. 3297	Boom difference approx. 6 pp (yield diff approx. 34 bps); bust 1778 difference approx. 35 pp (yield diff approx. 895 bps)
R7	Reputational effects are attenuated for bankers with short-run focus or married into wealth	Table V Panel B, p. 3301	Short-run focus: direct coeff -9.34*** (t = 3.09); interaction short-run focus x office value: -28.22* (t = 14.84); married-into-wealth x office value: -24.80* (t = 12.38)
R8	Banks with poor MBS performance suffered a 59% post-1770 decline in Amsterdam Bank of Exchange trading volume	Figure 6, p. 3303; Section VI.B Internet Appendix	Controlling for time and bank FE, post-1770 ABE trading volume 59% lower for below-median MBS performers

Overall (paper’s conclusion). Bank reputation can improve security quality in opaque markets, but only when bankers have substantial personal exposure to long-run reputational losses and are not focused on short-run profits. The evidence from the 18th-century Dutch plantation MBS market shows that high-reputation banks consistently originated better-quality mortgages and that investors suffered far smaller losses on their securities. The contrast with the 2000s RMBS literature is consistent with modern-era limited liability, bailout expectations, and short-term incentives undermining the same mechanism.

Theory / model

The paper presents a stylized partial-equilibrium model (Section III, pp. 3286-3290) with three types of agents: planters (borrowers), bankers, and investors. Each period a banker originates a loan and sells it to investors.

Planters. Plantation $p$ has fundamental value $F_p \in \{\underline{F}, \bar{F}\}$ with $\text{Prob}[F_p = \bar{F}] = \pi$. A planter takes a one-period loan $\lambda_{p,b,t}$ from banker $b$, paying interest $R_{b,t}$ and origination fee $\varphi$. The planter’s participation constraint pins the fee (eq. 1, p. 3286):

$$(1 - \varphi) \lambda_{p,b,t} \Omega \geq \bar{R} \lambda_{p,b,t}; \qquad \varphi \equiv \frac{\Omega - \bar{R}}{\Omega}. \tag{1}$$

Moral hazard: a planter can repay or defraud, taking $(1-\beta)F_p$ and leaving nothing. The incentive-compatibility constraint is (eq. 2, p. 3286):

$$F_p - R_{p,b,t} \lambda_p \geq (1 - \beta) F_p. \tag{2}$$

This defines maximum loan sizes $\bar{\lambda} \equiv \frac{\beta}{\bar{R}}\bar{F}$ (high type) and $\underline{\lambda} \equiv \frac{\beta}{\bar{R}}\underline{F}$ (low type) (eq. 3, p. 3286):

$$\bar{\lambda} \equiv \frac{\beta}{\bar{R}}\bar{F}; \qquad \underline{\lambda} \equiv \frac{\beta}{\bar{R}}\underline{F}. \tag{3}$$

Bankers. Each banker $b$ has discount factor $\delta_b$, outside activities yielding $\rho_b$ per period, and independent wealth $\omega_b$. A banker who misrepresents a low-type planter as high-type earns extra fees but loses fraction $\gamma = 1$ of outside activities permanently if discovered. The utility loss from falling below the threshold $\rho_b$ is quadratic (eq. 4, p. 3287):

$$U(y, \rho_b) = \begin{cases} y & \text{if } y \geq \rho_b \\ y - A(\rho_b - y)^2 & \text{if } y < \rho_b \end{cases}. \tag{4}$$

Market. The market continues each period with probability $\zeta_t$ (unknown; updated by Bayesian learning to $\mu_{t+1}$). Bankers learn about an impending end one period before investors.

Equilibrium types. Proposition 1 (pp. 3288-3289) shows that in equilibrium two types participate: Good (G) bankers who always provide $\underline{\lambda}$ honestly, and Mediocre (M) bankers who provide $\underline{\lambda}$ while the market is set to continue but misrepresent if they learn it is ending. The conditions are (eqs. 9-10, p. 3288):

$$G: \quad \varphi(\bar{\lambda} - \underline{\lambda}) \leq \frac{\delta_b}{1 - \delta_b} \left[\rho_b + A(\rho_b - \omega_b)^2\right] \tag{9}$$ $$M: \quad \varphi(\bar{\lambda} - \underline{\lambda}) - \frac{\delta_b}{1-\delta_b} \frac{(1-\delta_b \zeta_t)}{(1-\delta_b \zeta_t (1-\pi))} \varphi \bar{\lambda} \leq \frac{\delta_b}{1-\delta_b}\left[\rho_b + A(\rho_b - \omega_b)^2\right] \leq \varphi(\bar{\lambda}-\underline{\lambda}). \tag{10}$$

Key testable predictions. Prediction 2 (from Proposition 1, Lemma 1): high-reputation banks provide better loans; the gap is especially pronounced when the market is about to end. Prediction 5 (Lemma 1): the effect is attenuated for bankers with high $\omega_b$ (married into wealth) or low $\delta_b$ (short-run focus, i.e., discounting the future more heavily). Prediction 6 (by design): banks that sold worse MBS see a decline in other activities.

Method

The paper applies two main estimators: OLS with MBS fixed effects for the price regressions, and average causal mediation effect (ACME) analysis (Baron and Kenny (1986), Imai et al. (2011)) for the mediation analysis.

Reputation measure. Bank reputation $\rho_b$ is proxied by the rental value of a bank’s Amsterdam office block from the 1742 census (Oldewelt (1945)), reflecting outside business activities unrelated to plantation MBS. High-reputation: office value above the median. The measure is verified to correlate with city-government positions, ABE trading volume, and notarial deed counts (Table I, pp. 3274-3275).

MBS price regression (equation 11, p. 3294). For each auction transaction between 1768 and 1796:

$$P_{i,b,t} = \beta \rho_b + \eta_t + \varepsilon_{i,b,t}, \tag{11}$$

where $P_{i,b,t}$ is the price of MBS $i$ issued by bank $b$ in year $t$ as a percentage of par, $\rho_b$ is the reputation measure, and $\eta_t$ are auction-year fixed effects. Standard errors are clustered at the MBS level (46 clusters). Each transaction is weighted by the inverse of the number of auction transactions for that MBS times its market share, so $\beta$ captures the loss a hypothetical investor equally split between high- and low-reputation banks would have experienced.

Time-path specification (equation 12, p. 3296). To test Prediction 3, the regression is estimated separately for high- and low-reputation MBS:

$$P_{i,t} = \eta_t + \varepsilon_{i,t}, \tag{12}$$

where $\eta_t$ are annual averages for each group (same weights as eq. 11). This traces the price divergence over time, showing a small 6 pp gap during the boom (1769-1770) widening to approximately 35 pp in 1778.

Mediation analysis. The mediators (mortgage characteristics) are introduced sequentially into equation (11) to estimate the ACME (Imai et al. (2011)). Mediators are: timing of mortgage origination, fraction via nonelite agents, fraction to nonelite borrowers, and average LTF within an MBS. The joint ACME uses block-bootstrapping (10,000 resamples) to compute p-values. Results in Table IV, p. 3299-3300.

Empirical specifications

Mortgage quality regressions. The sample is all mortgages extended 1750-1770 by Amsterdam merchant banks issuing MBS (N = 315, 26 banks). Each observation is weighted by mortgage sum. The specification compares means for high- vs low-reputation banks before (1750-1768) and during (1769-1770) the boom, testing differences-in-differences (Table II, p. 3293). No instrument; identification relies on the timing of low-reputation banks entering the market and the differential change in quality across bank types during the boom period.

Key mortgage quality outcomes:

• LTV (mortgage amount / appraised value): high-rep boom ~0.56 vs. low-rep boom 0.62, diff -0.06*** (t = -2.67)
• Fraction nonelite borrowers: boom diff -0.35***, indicating high-rep banks maintained elite borrower screens during the boom
• LTF (mortgage / fundamental value, a debt-to-income analog): high-rep LTF increased 0.29*** (t = 2.66) during boom vs low-rep increase 0.50*** (t = 4.56), diff -0.21 (insignificant but economically large)

MBS price regression. Main sample: all MBS by Amsterdam merchant banks up to 1772 for which auction-price data are available (N = 46 MBS, 23 merchant banks; 4,605 auction transactions 1768-1796). Weighted OLS with auction-year FE and MBS-level clustered SE. The baseline result of 17.46 percentage points (t = 5.10) is robust to nine alternative reputation measures (Table III, cols. 1-9, p. 3295), block-bootstrapped SE, different regression weights, and annual-level aggregation.

Heterogeneity tests (Table V, pp. 3301-3302). The continuous office value interacted with short-run focus and married-into-wealth dummies:

• Short-run focus (partner died with minor heir) reduces the reputation effect to near zero (interaction coeff -28.22*, t = 14.84)
• Married into wealth reduces the reputation effect by more than 60% in the price regression (interaction coeff -24.80*, t = 12.38) and makes it statistically insignificant in the LTF regression

Standard errors are clustered at the bank level (23 or 24 clusters depending on spec).

Reputational losses (Figure 6, p. 3303). Post-1770 ABE trading volume is regressed on bank FE and time FE; above-/below-median MBS performers are separated on the estimated bank FE. The 59% post-1770 gap is the difference in log ABE volume between good- and poor-MBS-performance banks, controlling for time trends.

Datasets used

Dataset	Role in paper	Wiki page
Hand-collected plantation mortgage records (Amsterdam notarial archives, Suriname colonial archives)	Mortgage characteristics: LTV, borrower type, agent type, mortgage sum, appraisal values, loan-to-fundamental ratios; 1750-1770	No page yet
1742 Amsterdam property census (Oldewelt 1945)	Bank reputation measure: rental value of bank office block	No page yet
Amsterdam Bank of Exchange (Wisselbank) account books, 1765-1795	Bank trading volumes for reputational-loss test (half-yearly)	No page yet
MBS secondary-market auction prices (Amsterdam notarial archives)	MBS price outcomes: transaction prices 1768-1796, N = 4,605 transactions over 46 MBS	No page yet
Amsterdam estate tax records	Investor wealth distribution and portfolio holdings (N = 889 estates 1768-1796)	No page yet

Sample: Suriname plantation MBS market, 1753-1796. Core price regressions: 1768-1796, 46 MBS, 23 merchant banks, 4,605 auction transactions. Mortgage analysis: 1750-1770, 315 mortgages, 26 merchant banks.

When to read the full paper

Read the original if you are: studying the conditions under which bank reputation disciplines security quality in opaque markets; researching the history of financial innovation and the first MBS market; examining how banker incentive structures (limited liability, short-termism, wealth shielding) affect security issuance; or building on the mediation analysis framework to separate reputation effects from observable skill. Table III (p. 3295) contains the main price regressions; Tables IV and V (pp. 3298-3302) contain mediation and heterogeneity results; Figure 5 (p. 3297) traces the price gap over time.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6). This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. The CC BY-NC-ND 4.0 licence permits sharing with attribution for non-commercial purposes but no derivatives; the verbatim PDF is not hosted in this batch.

Attribution (CC BY-NC-ND 4.0). de Jong, Abe, Tim Kooijmans, and Peter Koudijs. “Going for Broke: Bank Reputation and the Performance of Opaque Securities.” The Journal of Finance 80, no. 6 (December 2025): 3263-3312. DOI: 10.1111/jofi.13503. © 2025 The Author(s). Licensed under CC BY-NC-ND 4.0. This page is a distilled summary by the Institute for Automated Research; it is not a reproduction of or derivative from the original article.