Peer Effects in Financial Expectations: Thornton (2026)

What this is. The paper’s core results, the identification strategy, and the empirical specifications: enough to know what was found and how, without reading all 22 pages. To replicate or extend, read the full source at the original.

TL;DR

Using 18 waves of the British Household Panel Survey (BHPS, 1991-2008) and an instrumental variables strategy adapted from Brown et al. (2008), Thornton (2026) provides causal evidence that neighborhood financial expectations positively influence individual financial expectations. The instrument is the average financial expectations of neighbors’ nonlocal family members, which affects a neighbor’s beliefs through family interaction but has no direct path to the focal individual. A one-standard-deviation increase in neighborhood financial expectations leads to a 2.8% increase in individual financial expectations (IV estimate), equal to roughly 31% of the corresponding family effect. Peer effects are larger for socially connected individuals, grow with time spent in a neighborhood (consistent with social interaction rather than sorting), and are informative only in neighborhoods with diversity in financial expectations and uniformity in income and voting behavior. These findings support the social transmission frameworks of Burnside et al. (2016) and Han et al. (2020). Individuals also act on their expectations: those expecting financial improvement are less likely to save.

Core results

Magnitudes and significance as reported; \*\*/\*\*\* = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Baseline OLS: neighborhood financial expectations are positively correlated with individual expectations	Table 2, col 1, p. 7	FINEXn coef = 0.555*** (t=30.01)
R2	With individual FE, year FE, and time-varying controls: large, significant peer effect remains	Table 2, col 2, p. 7	FINEXn coef = 0.272*** (t=16.88); 1-SD in FINEXn = 2.6% increase in FINEX; roughly 28% as large as the family effect (9.2%)
R3	IV causal estimate using nonlocal-family expectations as instrument	Table 6, col 2, p. 12	FINEXn coef = 0.505*** (t=2.60); restricted to different-region nonlocal family: 0.380*** (t=4.07, col 4)
R4	Reverse causality test: previous-neighborhood expectations do not predict current expectations	Table 7, col 2, p. 13	FINEXprev coef = 0.0484 (t=1.33), not significant
R5	Peer effects grow with time in neighborhood; income and voting similarity do not converge	Fig. 1, p. 9; Figs. 2-3, p. 10	Coef rises from 0.105 (insig) for 0 years to 0.264 (t=14.3) for 3+ years in neighborhood; income and voting coefficients flat or decreasing
R6	Expecting financial improvement is associated with a lower probability of saving	Table 8, col 2, p. 16	FINEX coef on Save = -0.0165*** (t=-7.37)
R7	Peer effects are larger for socially connected individuals	Fig. 4, p. 14; Figs. 5-7, pp. 15-16	Daily-talker subsample: coef = 1.01 (t=2.10); not significant for less-frequent interactors; same pattern for neighborhood-likers (Fig. 5) and organization members (Fig. 6)

Overall (paper’s conclusion). Financial expectations are causally transmitted among neighbors through social interaction, with a magnitude equal to roughly 31% of the family effect. The evidence is consistent across panel FE, IV, and IV robustness specifications; inconsistent with homophily (peer effects grow while income and political similarity do not); and supported by sociability heterogeneity (socially connected individuals show stronger transmission). Individuals also act on these expectations: optimistic individuals save less.

Theory / model

The paper does not propose a formal model. The central hypothesis is that an individual’s financial expectations (FINEX: whether the individual expects to be better off, about the same, or worse off financially in the coming year) are influenced by the financial expectations of her neighbors through social interaction.

The identification challenge is the reflection problem of Manski (1993): when neighbors have similar expectations, this correlation could arise from (1) endogenous social effects (social interaction), (2) contextual effects (shared local environment), or (3) correlated effects (similar individual characteristics). The paper tests three hypotheses:

• H1 (social interaction): Individuals take neighborhood expectations into account when forming their own; the coefficient $\beta_1$ in equation (1) captures a causal peer effect.
• H2 (homophily): Individuals sort into neighborhoods with like-minded residents; any observed correlation reflects selection rather than transmission.
• H3 (contextual / correlated): A shared local environment (e.g. the local labor market) drives correlated expectations; there is no individual-level transmission.

H2 is tested via the time-in-neighborhood design (Section 4.2): if sorting drives the result, peer effects should be strongest when individuals first move (closest to their selection decision) and weaken thereafter as neighbors diverge from the mover’s baseline. The opposite pattern is found. H3 is addressed by the IV strategy and by the fact that income and political-preference similarity within neighborhoods do not grow over time (Figs. 2-3 on p. 10).

The paper also tests a joint hypothesis (Section 4.8): that survey expectations reflect actual beliefs and that individuals act on them. If this joint hypothesis holds, individuals expecting improvement should save less. This is confirmed in Table 8.

Method

The paper applies two estimators: panel OLS with individual and year fixed effects, and two-stage least squares (2SLS) with the nonlocal-family instrument.

Panel fixed effects. The main estimating equation (p. 6, Eq. 1) is:

$$\text{FINEX}_{it} = \gamma_i + \gamma_t + \beta_1 \, \text{FINEXn}_{it} + \lambda_i + \varepsilon_{it} \tag{1}$$

where $\gamma_i$ are individual fixed effects absorbing time-invariant characteristics (race, religion, baseline sociability), $\gamma_t$ are year fixed effects absorbing sample-wide trends, $\text{FINEXn}_{it}$ is the average financial expectation of individual $i$‘s neighbors in year $t$ (excluding $i$), $\lambda_i$ is a vector of time-varying controls (income, education, marital status, vote intention, job industry), and $\varepsilon_{it}$ is clustered at the interview-area (neighborhood) level throughout.

IV strategy. The instrument for $\text{FINEXn}_{it}$ is the average financial expectation of neighbors’ nonlocal family members, denoted $f\!amFINEXn_{it}$. This instrument builds on the Brown et al. (2008) design: nonlocal family members are likely to influence their relative’s expectations through family interaction, but are not subject to the same local environment as the focal individual. The first-stage regression is:

$$\text{FINEXn}_{it} = \gamma_i + \gamma_t + \delta \cdot f\!amFINEXn_{it} + \lambda_i + \nu_{it}$$

The instrument is constructed in two ways: (a) nonlocal family = family members living outside the focal neighborhood; (b) nonlocal family = family members living in a different UK region (19 regions), the more demanding robustness specification. The first-stage $t$-statistic in the full-controls specification is 3.61 (Table 4, col 2, p. 10), exceeding the Lee et al. (2022) tF critical value of 3.02 at the 5% level ($F$-statistic = 13.03).

Sociability subsamples. To provide additional evidence for social interaction as the mechanism, the IV specification from Table 6, col 2 is re-run on subsamples split by four sociability proxies: frequency of talking with neighbors (FRNA), opinion of neighborhood (Lknbr), local organization membership (Org), and desire to move (Lkmove). This approach, similar to Hong et al. (2004), uses sociability variation to test whether more connected individuals exhibit larger peer effects.

Empirical specifications

Main panel OLS (R1, R2). Equation (1) is estimated with no controls (Table 2, col 1, $N$ = 218,149) and with individual FE, year FE, and time-varying controls (col 2, $N$ = 207,362). Standard errors clustered at the neighborhood level throughout. FINEX is coded 1 (better off), 0 (same), -1 (worse off). The focal individual is excluded from the neighborhood average.

IV 2SLS (R3). Table 6 (p. 12) reports four 2SLS specifications. Columns (1)-(2) define nonlocal family as living outside the focal neighborhood; columns (3)-(4) restrict to a different UK region. Columns (1) and (3) include only wealth as a time-varying control; columns (2) and (4) add the full set of controls plus individual and year FE. The 2SLS coefficient is stable across specifications: from 0.612*** (col 1) to 0.380*** (col 4, most demanding).

Reverse causality test (R4). Table 7 (p. 13) regresses individual FINEX on average financial expectations in the individual’s previous neighborhood (FINEXprev), using the same controls as Table 2, col 2. A significant coefficient would indicate that the IV result is capturing ties to the previous neighborhood rather than the current one. The coefficient is 0.0484 (t = 1.33), not significant.

Time-in-neighborhood trend (R5). Using the specification from Table 2, col 2, four subsample regressions are run on individuals grouped by years-in-neighborhood (0, 1, 2, 3+). The peer-effect coefficient grows monotonically (Fig. 1, p. 9). The same subsampling is applied with income and vote intention as outcomes (Figs. 2-3, p. 10); those coefficients show no convergence, ruling out general assimilation to the local environment as the driver.

Savings regression (R6). Table 8 (p. 16) regresses a binary savings variable (Save = 1 if the individual saved over the past year) on individual FINEX, with individual FE, year FE, and time-varying controls. Standard errors clustered at the neighborhood level.

Sociability subsamples (R7). The IV specification from Table 6, col 2 is re-run separately for each level of each sociability proxy (Figs. 4-7, pp. 14-16). Peer effects are statistically significant only for the most socially connected subgroup in each proxy (daily talkers: coef = 1.01, t = 2.10; neighborhood-likers: coef = 0.583, t = 2.86; organization members: coef = 1.10, t = 2.38; non-movers: coef = 0.72, t = 2.79).

Datasets used

Dataset	Role in paper	Wiki page
British Household Panel Survey (BHPS), waves 1-18, 1991-2008; UK Data Service SN 5151-2	All financial expectation measures (FINEX, FINEXn, famFINEXn), sociability proxies (FRNA, Lknbr, Org, Lkmove), savings dummy (Save), neighborhood identifiers (interview area, IVIA), and demographic controls	no page yet

Sample: 18 annual waves, approximately 10,000 initial participants (later adding subsamples in 1997 and 1999), 250 interview areas averaging 41 residents each. Main panel-OLS samples: 207,362 (full controls, Table 2 col 2) to 218,149 (baseline, Table 2 col 1) person-year observations.

When to read the full paper

Read the original if you are: studying how beliefs (rather than behavior) spread through social networks; replicating or extending the nonlocal-family IV strategy of Brown et al. (2008); analyzing how sociability moderates peer effects (Figs. 4-7); testing whether peer learning is informative only in specific neighborhood types (Figs. 9-11 on expectation, voter, and income polarization); or connecting financial expectations to household saving behavior.

Attribution and rights

Source: peer-reviewed, Journal of Empirical Finance 87 (2026) 101712. Paywalled; all rights reserved, © 2026 Elsevier B.V. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. Extract-only; the verbatim PDF is not hosted.

Thornton, Joshua. “Peer effects in financial expectations.” Journal of Empirical Finance 87 (2026) 101712. DOI: 10.1016/j.jempfin.2026.101712.