Marginal Propensity to Consume and Personal Characteristics: Evidence from Bank Transaction Data and Survey

Layer 1 — Overview

Research Question. This paper asks whether heterogeneity in the marginal propensity to consume (MPC) stems from temporary circumstances (e.g., transient wealth shocks that tighten liquidity) or persistent personal characteristics (e.g., high time discount rates or strong risk aversion that permanently shape saving behavior). Because liquidity constraints are endogenous — they can reflect either bad luck or impatient preferences — disentangling these two sources requires independently measured individual characteristics, which are not available in standard transaction datasets.

Data and Setting. The study combines two data sources drawn from Mizuho Bank, one of Japan’s three largest banks (approximately 24 million individual accounts). First, weekly bank account transaction data for January 2019 to November 2022 covering all outflows (ATM withdrawals, credit card debits, utility payments, interbank transfers) for the approximately 5,282 survey respondents. Second, a bespoke survey conducted in November–December 2022 among 400,000 randomly selected salary-receiving account holders (response rate 1.32%, yielding 5,282 usable observations). The survey elicits the Arrow–Pratt measure of absolute risk aversion, quantitative time discount rates for one-week, one-year, and ten-year horizons, self-reported liquidity constraints, homeownership, education, age, and gender, among other variables.

Three Income Shocks. MPC is estimated against three distinct income events: (1) the Japanese government’s Special Cash Payments (SCP) — a 100,000 JPY (approximately 800 USD) per-person lump-sum transfer during COVID-19, likely transitory, unexpected, and nearly randomly timed across municipalities due to administrative bottlenecks; (2) regular salary receipts (recurring, expected in both timing and amount); and (3) semi-annual bonus payments (received twice yearly, with timing known in advance but amount largely unknown — intermediate between SCP and salary in terms of expectedness).

Estimation Strategy. A two-way fixed effects regression with event-study leads and lags (windows of five weeks before and after each income event) is used to estimate consumption responses. Individual and week fixed effects absorb time-invariant heterogeneity and aggregate shocks (including COVID-19 emergency declarations). Standard errors are clustered at the individual level. For heterogeneity analysis, the income shock variable is interacted with individual characteristics from the survey (treated as proxies for persistent characteristics) and with time-varying log wealth and a liquidity constraint dummy (wealth below one-twelfth of annual income, proxying temporary circumstances).

Main Findings — Average MPC. Across all three income types, the on-impact MPC (week of receipt) is approximately 0.2: specifically γ₀ = 0.23 for the SCP (significant at 5%), 0.20 for salary, and 0.22 for bonus. When estimated jointly in a single regression, coefficients are γ_SCP = 0.21, γ_salary = 0.19, and γ_bonus = 0.21. This uniformity holds despite the sharply different properties of these shocks (transitory-unexpected vs. regular-expected vs. semi-known).

Main Findings — Heterogeneity. Significant heterogeneity in MPC is found primarily in the bonus subsample, where statistical power is greatest. The following cross-term coefficients are significant at the 5% level in the multivariate specification: (a) liquidity constraint dummy — positive and significant, indicating that individuals temporarily below one month’s income in deposits spend a larger fraction of their bonus, with a one standard deviation increase raising MPC by 0.094 (9.4 percentage points); (b) time discount rate (quantitative measure) — positive and significant, with a one standard deviation increase in impatience raising MPC by 0.084; (c) risk aversion (quantitative Arrow–Pratt measure) — positive and significant, conditional on controlling for wealth and liquidity, with a one standard deviation increase raising MPC by 0.031; (d) education — negative and significant irrespective of wealth/liquidity controls, with a one standard deviation increase in education reducing MPC by 0.041.

These magnitude estimates are sizable relative to the baseline MPC of approximately 0.2. For SCP and salary shocks, cross-term coefficients are uniformly insignificant at the 5% level, which the author attributes partly to smaller sample sizes and shorter observation windows for the SCP subsample.

Scope Conditions. The sample consists of Mizuho Bank account holders who receive salary payments directly into their Mizuho account, overrepresenting metropolitan areas and salaried workers relative to the national census. Wealth at Mizuho captures only deposits at that institution and excludes securities accounts, postal savings, and intra-household transfers. Age and gender do not yield significant cross-term coefficients in any specification; the self-reported survey measure of liquidity constraints (ability to cover one month’s income by drawing on savings, assets, or borrowing) is also insignificant, in contrast to the transaction-based liquidity constraint dummy.

Layer 2 — Q&A

Q1. Why is separating temporary circumstances from persistent characteristics important for MPC estimation? Liquidity constraints — the standard proximate predictor of high MPC — are endogenous. An individual may be liquidity-constrained because of a temporary adverse income shock (bad luck) or because of persistently high impatience (high time discount rate) that leads to chronically low saving. If policy evaluation treats all constrained households symmetrically, it conflates these two very different channels. The paper follows Jappelli and Pistaferri (2020), Gelman (2021), and Aguiar, Bils, and Boar (2021) in arguing that both channels matter and that their relative contributions need empirical separation.

Q2. Why are Japanese bonuses particularly well-suited to identifying MPC heterogeneity? Bonuses are paid semi-annually to most regular employees in Japan (accounting for roughly 15–30% of annual income), with timing known in advance but amount largely unknown until receipt. This intermediate nature — partially anticipated in timing but uncertain in magnitude — provides meaningful variation in consumption responses across individuals while maintaining a clean event-study design. The bonus subsample (3,722 individuals who received a bonus at least once) is also large enough to detect cross-term effects that are statistically insignificant in the SCP subsample (2,446 individuals) and in the salary analysis, likely due to greater statistical power.

Q3. How is the Arrow–Pratt measure of risk aversion constructed from the survey? Respondents are asked whether they would purchase a lottery ticket at prize value Z = 100,000 JPY and price p = 10,000 JPY for varying winning probabilities α. The threshold α at which a respondent switches from accepting to rejecting identifies their risk attitude. The absolute risk aversion σ = −U’’/U’ is then calculated as (αZ² − 2αZp + p²) / (2(αZ − p)). This yields σ ranging from −4.5 (when α = 0.01, i.e., risk-loving) to 0.891 (when α = 1, i.e., refusing to buy even at a 90% win probability). Risk neutrality corresponds to σ = 0 (at α = 0.1).

Q4. How are time discount rates measured, and what is the range? Respondents are asked the minimum amount X they would require to wait one week, one year, or ten years to receive a payment instead of receiving 100,000 JPY one week from now (using a one-week anchor to address hyperbolic discounting). The discount rate is calculated as r = X/100,000. The range is 0.01 (X = 100 JPY) to 100 (X = 10,000,000 JPY, i.e., would not wait even for 1,100,000 JPY in ten years). The unweighted average across one-week, one-year, and ten-year horizons is used as the composite discount rate in the multivariate specifications.

Q5. What is the transaction-based liquidity constraint dummy, and how does it differ from the survey-based measure? The transaction-based dummy equals one if end-of-month deposits at Mizuho Bank (the previous month) are below one-twelfth of the individual’s annual income — i.e., if the individual holds less than one month’s equivalent income in liquid deposits. This is a time-varying measure. The survey-based measure asks respondents to self-report whether they could cover one month’s income by drawing on savings, selling assets, or borrowing. The transaction-based measure is significant at the 5% level in the bonus and salary heterogeneity regressions, while the survey-based measure is insignificant, indicating that the precise definition and data source of the liquidity constraint measure matters materially for detecting its effect on MPC.

Q6. What are the estimated on-impact MPC values for each income shock, and how stable are they across robustness checks? The point estimates from the event-study regression (γ₀) are: 0.23 for SCP in the baseline sample (SCP recipients in 2020, N = 2,446 individuals), 0.20 for salary (all 5,282 survey respondents), and 0.22 for bonus (3,722 bonus recipients). In a robustness specification restricting to only year-2020 data for the SCP, γ₀ = 0.235; using cash withdrawals from ATMs as a proxy for consumption instead of total outflows, γ₀ = 0.162 for SCP. In a joint regression including all three income types simultaneously, γ_SCP = 0.21, γ_salary = 0.19, and γ_bonus = 0.21. The SCP MPC for the smaller second-wave subsample (200 individuals, 2021–22) is 0.104 and insignificant, consistent with insufficient statistical power rather than a structural difference.

Q7. Why is the similarity in MPC across the three shock types potentially surprising, and what does the paper say about it? Standard theory predicts divergent MPCs: transitory unexpected windfalls (SCP) should have a higher MPC than permanent salary changes under the permanent income hypothesis, while Ricardian equivalence might reduce the MPC to fiscal transfers like the SCP if households anticipate future tax increases. The paper finds the MPCs are approximately equal (around 0.2 across all three types), and if anything the SCP MPC is slightly higher than the salary MPC. The paper acknowledges this uniformity without offering a structural explanation, using it primarily as a robustness check on the baseline estimate rather than a substantive puzzle to resolve.

Q8. Which personal characteristics are significantly associated with higher MPC, and in which income shock samples? In the multivariate heterogeneity regression, significant cross-term coefficients at the 5% level are found exclusively in the bonus subsample (columns 5–6 of Table 6): the quantitative risk aversion measure (positive, coefficient 0.042–0.049), the quantitative discount rate (positive, coefficient 0.004), and education (negative, coefficient −0.034 to −0.037). The liquidity constraint dummy (transaction-based) is also positive and significant for bonuses. In the univariate robustness regressions (Table 7), the own-house dummy is negative and significant at 5% for bonuses (controlled and uncontrolled); discount rates for one-week and ten-year horizons are positive and significant at 5% for bonuses; risk aversion A (direct self-report) is negative and significant at 5% for SCPs in the uncontrolled specification.

Q9. Do age and gender matter for MPC heterogeneity? No. In all specifications across all three income shock types, the cross-term coefficients on age and the male dummy are uniformly insignificant at the 5% level. The lack of significance for age and gender is noted as a notable result, since both are commonly used demographic proxies in heterogeneous agent models that assume they reflect economically meaningful differences in consumption behavior.

Q10. How does the paper quantify the economic magnitude of each significant heterogeneity factor? Table 8 reports the product of each cross-term coefficient and the standard deviation of the corresponding variable. For the bonus subsample: a one standard deviation increase in the liquidity constraint dummy raises MPC by 0.094 (9.4 percentage points); a one standard deviation increase in the discount rate raises MPC by 0.084; a one standard deviation increase in risk aversion raises MPC by 0.031; and a one standard deviation increase in education reduces MPC by 0.041. All four magnitudes are described as sizable relative to the baseline MPC of approximately 0.2 (20%).

Q11. Why does the paper focus on bonuses for the heterogeneity analysis rather than the SCP? The SCP events provide cleaner identification of transitory, exogenous income shocks (near-random timing due to municipal administrative bottlenecks, as documented by Kubota, Onishi, and Toyama 2021), but the subsample of SCP recipients is smaller (2,446 in 2020, 200 in the second wave), reducing statistical power for detecting heterogeneity in cross-term coefficients. The salary sample is large (5,282 individuals) but salaries are expected, recurring, and may partially update permanent income, complicating interpretation of cross-term estimates. Bonuses offer a balance: a relatively large subsample (3,722) and a partially unexpected income component, making them the most informative sample for heterogeneity analysis.

Q12. What are the main caveats and limitations the paper identifies? Four caveats are noted. First, the personal characteristics from the survey — including time discount rates and risk aversion — are treated as exogenous, but they may themselves be endogenous to economic circumstances or short-term conditions at the time of the survey. Second, only Mizuho Bank deposits are observed; financial assets at other institutions (securities, postal savings) are missing, meaning the liquidity constraint measure understates true wealth for some respondents. Third, the sample is tilted toward metropolitan salaried workers and toward wealthier individuals compared to the full Mizuho customer base (median log wealth of 7.4 vs. 5.9 in Kubota et al. 2021). Fourth, the multiple-testing problem is acknowledged: with many cross-term tests conducted, some rejections of the null at the 5% level may be spurious.

Key Concepts

Marginal Propensity to Consume (MPC, on-impact). In this paper, MPC is operationalized as the coefficient γ₀ from the two-way fixed effects event-study regression — specifically, the fraction of an income shock spent during the same week the shock is received, estimated from total bank account outflows. This is a weekly, within-account measure, not a lifetime or annual consumption response.

Arrow–Pratt Absolute Risk Aversion (σ). A quantitative measure of risk preferences computed from the paper’s survey by eliciting the probability threshold α at which a respondent is indifferent between buying and not buying a lottery with prize Z = 100,000 JPY and price p = 10,000 JPY. Calculated as σ = (αZ² − 2αZp + p²) / (2(αZ − p)). Ranges from −4.5 to 0.891 in the sample, with σ = 0 indicating risk neutrality.

Time Discount Rate (r). Measured by asking respondents the minimum additional amount X (beyond 100,000 JPY) they would require to delay receipt by one week, one year, or ten years, with r = X/100,000. The paper uses the unweighted average of three horizon-specific rates as a composite measure. Ranges from 0.01 to 100 in the sample. Used as a proxy for impatience or myopia — a persistent personal characteristic.

Liquidity Constraint Dummy (transaction-based). A time-varying binary indicator that equals one if individual i’s end-of-month Mizuho Bank deposit balance in month t−1 is below one-twelfth of annual income at t−1 — i.e., less than one month’s equivalent income in liquid deposits. Distinguished in the paper from a survey-based self-report of liquidity constraints, which is found to be insignificant.

Special Cash Payment (SCP). The Japanese government’s COVID-19 pandemic transfer program, providing 100,000 JPY (approximately 800 USD) per person in 2020 (universal) and 100,000 JPY per child in 2021–22 (restricted to households with children under 18 and income below 9.6 million JPY annually). Used in this paper as a transitory, salient, and largely unexpected income shock because municipal administrative bottlenecks made the exact timing unpredictable and nearly random across households.

Two-Way Fixed Effects Event-Study Regression. The paper’s primary estimator, which includes individual fixed effects (controlling for time-invariant person-level heterogeneity) and week fixed effects (absorbing aggregate shocks such as COVID-19 emergency declarations and seasonal patterns). Event-study leads and lags (k = −5 to +5 weeks around each income receipt) allow pre-trend testing and tracing of the dynamic consumption response. Normalized to γ_{−1} = 0.

MPC Heterogeneity Cross-Term. A regression augmentation (equation 3 in the paper) in which the contemporaneous income shock X⁰_{it} is interacted with individual characteristic Z_{it}. The coefficient δ on this cross-term identifies how the MPC varies with Z — the marginal effect of characteristic Z on the MPC. Persistent characteristics (e.g., risk aversion, discount rate, education from the survey) and temporary circumstances (e.g., log wealth, liquidity constraint dummy from transaction data) are included as separate Z variables.