# The consumption response to unemployment: evidence from French bank account data

Odran Bonnet, François Le Grand, Tom Olivia, Xavier Ragot, Lionel Wilner

<figure><img src="image_1.png" /></figure>

## To cite this version:

Odran Bonnet, François Le Grand, Tom Olivia, Xavier Ragot, Lionel Wilner. The consumption response to unemployment: evidence from French bank account data. 2024. hal-05316543

HAL Id: hal-05316543

https://insee.hal.science/hal-05316543v1

Preprint submitted on 15 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# The consumption response to unemployment: Evidence from French bank account data

## Documents de travail

N° 2024-23 – Octobre 2024

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

Odran BONNET
François LE GRAND

Tom OLIVIA
Xavier RAGOT

Lionel WILNER
<figure><img src="image_5.png" /></figure>
Institut national de la statistique et des études économiques

2024/23

# The consumption response to unemployment : Evidence from French bank account data*

ODRAN BONNET<sup>Σ</sup>, FRANÇOIS LE GRAND<sup>†</sup>, TOM OLIVIA<sup>†</sup>, XAVIER RAGOT<sup>§</sup>, LIONEL WILNER<sup>‡</sup>

08 octobre 2024

Département des Études Économiques – Timbre G201
88, avenue Verdier – CS 70 058 – 92 541 MONTROUGE CEDEX – France
Tél. : 33 (1) 87 69 59 54 – E-mail : d3e-dg@insee.fr – Site Web Insee : http://www.insee.fr

*Ces documents de travail ne reflètent pas la position de l'Insee et n'engagent que leurs auteurs. Working papers do not reflect the position of INSEE but only their author's views.*

<footnote>* We are extremely grateful to La Banque Postale for sharing the data with us, and in particular to key employees for their precious help. All individual data used in this analysis have been anonymized and no single customer can be traced in the data. All data processing has been conducted following the banks' strict data privacy guidelines. We thank participants at IAAE (London, 2022) and EEA (Milano, 2022) conferences as well as audiences at the workshop Crest-Insee (Paris, 2023) and at the Insee-DEE Seminar (Paris, 2024). We are also grateful to Pierre Cahuc, Pauline Givord and Marine Guillerm for useful suggestions. We also would like to express our gratitude to Matéo Moisan for his work, which significantly contributed to the completion of this article.</footnote>

<footnote>ΣBonnet (Insee): odran.bonnet@insee.fr</footnote>

<footnote>†Le Grand (Rennes School of Business): francois.le-grand@rennes-sb.fr</footnote>

<footnote>†Olivia (Insee): tom.olivia@insee.fr</footnote>

<footnote>§Ragot (Sciences Po, CNRS, OFCE): xavier.ragot@sciencespo.fr</footnote>

<footnote>‡Wilner (Crest): lionel.wilner@ensae.fr</footnote>
Lorsqu'ils perdent leur emploi, les ménages puisent-ils dans leur épargne
ou réduisent-ils leurs dépenses ? – une approche par des données de
comptes bancaires

La perte d'un emploi entraîne une diminution des revenus, car les allocations chômage ne compensent que partiellement la perte de salaire. Cette étude décrit comment les ménages s'adaptent financièrement à cette baisse de revenu. Puisent-ils dans leur épargne pour maintenir leur niveau de consommation ou sont-ils contraints de réduire leurs dépenses ? Ce travail utilise des données de comptes bancaires pour mesurer comment les ménages ajustent leurs dépenses suite à la perte d'un emploi. Nos résultats indiquent que la consommation diminue d'un montant équivalent à 36 % de la perte de revenu sur les six premiers mois de chômage. Plus la période de chômage se prolonge, plus les ménages réduisent leur consommation et moins ils sont enclins ou capables de puiser dans leur épargne. Un mois après la perte d'emploi, la consommation diminue peu, l'équivalent de 5 % de la perte de revenu mensuel, mais six mois après la perte d'emploi initiale la consommation diminue beaucoup plus, l'équivalent de 46 % de la perte de revenu mensuel. La diminution de la consommation est plus forte chez les ménages détenant peu d'actifs liquides, mais dépend peu du revenu du ménage.

Mots-clés : Propension marginale à consommer, Assurance chômage, Données sur les comptes bancaires

Codes JEL : C55; D12; D15; H50

The consumption response to unemployment -
Evidence from French bank account data

The loss of a job results in a reduction in income, as unemployment benefits provide only partial compensation for the loss of wages. This study investigates the financial adaptations made by households in response to such circumstances. Do they draw on their savings to maintain their consumption level or are they forced to reduce their expenditures? This paper uses French high frequency bank account data to measure how spending responds to job loss. Our findings indicate that, in the first six months of unemployment, 36% of the income loss is offset by a reduction in spending, while the remainder is primarily compensated by a decrease in liquid savings. The longer the unemployment spell, the greater the reduction in spending. Only 5% of the income loss is offset by a reduction in spending one month after the job loss. However, this figure reaches 46% six months after the initial job loss. The response depends on the quantity of liquid assets held by households, while being less influenced by income.

Keywords: Marginal propensity to consume, Unemployment insurance, Bank account data

JEL Code: C55; D12; D15; H50
# 1 Introduction

The loss of a job leads to a significant and prolonged reduction in income, which can lead to a sharp fall in consumption.<fnref n="1" /> Modern welfare states have introduced measures to mitigate this negative income shock: unemployment benefits (UB), such as the Aide au Retour à l'Emploi (ARE) in France, act as a replacement income, partially compensating workers for their lost earnings. However, the rest of the burden is borne by the workers. Are they able to insure themselves through their savings, or do they have to reduce their consumption as a consequence? Answering these questions allows us to assess the extent to which unemployment insurance fulfils its role as a safety net during periods of unemployment.

Despite the considerable attention paid to unemployment in both public and academic debates, empirical evidence on how household finances are affected by job loss remains limited. How do households adjust their consumption and saving behaviour during periods of unemployment? Answering this question requires access to high-frequency data on income, spending and savings, which remains difficult to obtain. However, the recent availability of bank account data to researchers in several countries has facilitated new insights. With access to bank data, it is possible to estimate the marginal propensity to consume (MPC) in response to an unemployment shock, thereby quantifying the reduction in consumption associated with income losses due to spells of unemployment.

The contribution of this article is to provide a month-by-month analysis of unemployment spells in France, using high-frequency data from the

<footnote n="1">¹Other channels than the purely monetary ones may prevail; a self-assessed measure of welfare, subjective well-being, has been shown to fall in the aftermath of job loss (Clark et al., 2008).</footnote>
last quarter of 2020 to the first quarter of 2024. This study quantifies the
role of private insurance mechanisms, such as savings, in mitigating income
losses. We summarise our findings on the spending response to this shock by
estimating the corresponding MPC, both on average and across observable
characteristics such as age, income and liquidity. In addition, we estimate the
timing of the consumption adjustment as a function of the duration of unem-
ployment. For identification purposes, our analysis is limited to unemployed
persons receiving benefits.

When unemployed, households draw on their personal savings to limit
the fall in consumption. Hence, one month after job loss, consumption falls
by only 2%. The longer the period of unemployment, the more households
reduce their consumption, and the less they are willing or able to draw on
their savings. After six months of unemployment, consumption is 15% lower
than before job loss. These adjustments imply that over the first six months
of unemployment, 36% of the drop in income was offset by a reduction in
consumption (i.e., MPC is equal to 0.36). There is considerable heterogeneity
in the responses according to the quantity of liquid assets held, with MPC
ranging from 0.19 for the top 25% of liquidity holders to 0.56 for the bottom
25%. Furthermore, we examine the effect of the income increase resulting
from the full and final settlement in the last month of employment prior
to job loss. The additional income is not entirely allocated to savings for
mitigating the impact of future unemployment, as it leads to an increase in
spending. However, the marginal propensity to consume (MPC) is relatively
low, at 0.20.

**Literature** This article draws on three distinct bodies of literature. First, the studies most closely related to our work are those that examine job loss
using bank account or app data, as these studies are able to analyse infra-
annual consumption adjustments. This literature includes Ganong and Noel
(2019) in the US, Gerard and Naritomi (2021) in Brazil and Andersen et al.
(2023) in Denmark. Empirical evidence suggests an average MPC up to 24
months after a job loss of around 0.3 in the US or Denmark and 0.2 for
consumer durables in Brazil. Using annual administrative data, Fagereng,
Onshuus, and Torstensen (2024) find an MPC of 0.4 following an unemploy-
ment shock. Second, many papers have examined the issue of heterogeneity
in the MPC using different income shocks, such as lotteries or stimulus pay-
ments. The role of liquidity has been particularly emphasised by Kaplan,
Violante, and Weidner (2014). Third, the literature on the optimal level of
unemployment insurance (UI) has tried to operationalise the Baily-Chetty
formula (Baily, 1978) on the basis of sufficient statistics. Chetty (2006)
states that the optimal level of UI is determined by three parameters: “(1)
the elasticity of unemployment durations with respect to benefits, which cap-
tures the moral hazard cost of benefit provision due to behavioral response;
(2) the drop in consumption as a function of UI benefits, which quantifies
the consumption-smoothing benefits; and (3) the coefficient of relative risk
aversion, which reflects the value of having a smoother consumption path”.
Since Gruber (1997), many papers have tried to quantify the second term, re-
lated to consumption reduction: a partial list includes Browning and Crossley
(2001), Engen and Gruber (2001), Chetty (2008), Hendren (2017), Kolsrud
et al. (2018), and Landais and Spinnewijn (2021). Our paper contributes to
this literature by estimating the infra-annual consumption response in the
context of France.

The rest of the paper is organized as follows. Section 2 presents the
institutional setting and section 3 presents our data, including comparisons
with external sources to address both representativeness and completeness concerns. Section 4 presents our empirical methods and section 5 presents the main results. Section 6 presents additional analyses focusing on the importance of access to liquidity and the duration of the unemployment spell considered. Section 7 addresses robustness concerns. Section 8 concludes.

## 2 Institutional setting

In France, eligibility for unemployment insurance (UI) requires a minimum period of prior employment.<fnref n="2" /> Specifically, individuals must have been employed for at least six months during the reference period of affiliation. This reference period corresponds to 24 months preceding the termination of their employment contract for individuals below 52 years old and 36 months for those aged 53 and older. Only involuntary leavers, i.e. workers who did not deliberately leave their jobs, can claim UB.<fnref n="3" /> Depending in particular on collective agreements and the level of seniority, the full and final settlement may include additional benefits such as severance pay or compensation for untaken leave.

UB are characterized by two parameters, their duration and their amount.
The duration for which an unemployed gets benefits is equal to the number

<footnote n="2">²France has been reforming its unemployment insurance rules between 2019 and 2021, affecting eligibility, duration, and benefit levels. The latest measures, which affect the calculation of duration and replacement rate, came into force on October 1, 2021. Here, we outline the new rules that apply to the majority of our sample, with the earliest entry into unemployment occurring in July 2021 and the latest in August 2023. The main impact of the reform is a reduction in the value of unemployment benefits and an extension of their duration, which varies by age.</footnote>

<footnote n="3">³There are a few exceptions to this rule, and some resignations, justified by professional or family imperatives, may be considered legitimate. Specifically, the unemployed must prove “a termination of the employment contract by dismissal, or the termination of a fixed-term employment contract (CDD), or a contractual termination, or resignation for a legitimate reason”, *European Commission*.</footnote>
of calendar days (including holidays and non-working days) between the first day of the first employment contract and the last day of the last employment contract in the reference period of affiliation. This period is called the period of affiliation (in contrast to the reference period of affiliation). However, non-working days (inter-contractual periods) are limited to 75% of the number of working days.

The amount, the daily allowance, is calculated based on the daily reference wage. The daily reference wage (DRW) is calculated by dividing the earnings<fnref n="4" /> received during the individual's period of affiliation by the total number of calendar days in that period.

$$DRW = \frac{\text{earnings in the period of affiliation}}{\text{nb calendar days in the period of affiliation}}$$

Then the daily allowance (DA) is equal to the highest amount between 57% of the daily reference wage and 40,4% of the daily reference wage plus €12,12 × α<fnref n="5" />, where α corresponds to the part-time coefficient.⁶⁷ Moreover, the minimal allowance is set to €29.56 × α and in no case can the allowance exceed 75% of the daily reference wage nor €256.96 by day. To sum up, the

<footnote n="4">⁴The earnings considered exclude various bonuses, including reimbursement of business expenses, insecurity bonuses, and payments for untaken holidays.</footnote>

<footnote n="5">⁵Values are those in effect on October 1, 2021. They have been revalued over the period in line with inflation. All calculation rules can be found here https://www.cdg74.fr/sites/default/files/atoms/files/circulaire_unedic_2021-13_du_19_octobre_2021_reglementation_dassurance_chomage.pdf.</footnote>

<footnote n="6">⁶In the case of part-time work, α is equal to the number of hours you work by week divided by the legal weekly working hours (35 hours).</footnote>

<footnote n="7">⁷Since December 1, 2021, a measure reducing the allowance applies to beneficiaries under age 57 receiving a certain level of allowance (€91.02 per day) after an eight-month period of compensation (243 days).</footnote>
daily allowance is calculated as follows:

$$DA = \min \left\{ \begin{array}{l} \max \left\{ \begin{array}{l} \text{€}29.56 \times \alpha \\ 40.4\% \times DRW + \text{€}12.12 \times \alpha \\ 57\% \times DRW \end{array} \right. \\ \text{€}256.96 \\ 75\% \times DRW \end{array} \right. $$

The allowance is paid once a month by multiplying the daily allowance by the number of calendar days in the month (28, 29, 30 or 31).

In the event of reemployment, it is possible to combine both unemployment benefits and salary. However, this combination is limited to the monthly amount of the reference wage: the total monthly amount received by the beneficiary (wage + unemployment benefits) cannot exceed the reference wage on which the benefit amount was calculated.

# 3 Data construction

The database used in this study originates from La Banque Postale (LBP hereafter), a public bank established in 2006 within the postal group La Poste, the historical monopoly responsible for mail delivery; this bank serves nearly 11 million customers. The construction of key variables follows methodologies from previous studies employing similar data (Baker, 2018; Ganong and Noel, 2019; Andersen et al., 2023). We employ transaction-level data on card payments,<fnref n="8" /> paper checks, cash withdrawals, cash deposits, bank transfers, and direct debits, with each transaction recorded in euros. Additionally, we have access to balance sheet data, including end-of-month balances on

<footnote n="8">⁸It includes debit and credit cards. In France, the use of credit cards is limited, accounting for less than 10% of bank cards.</footnote>
deposit and various savings accounts,<fnref n="9" /> as well as life insurance, stocks, and
credits (consumer loans and mortgage loans). Deposit accounts encompass
joint accounts. We aggregate customers sharing a joint account into the same
household, making the household our unit of observation. The data employed
is high-frequency, containing transaction-level information timestamped and
aggregated daily, while balances are available on a monthly basis. Finally,
we observe various socio-demographics, including age, sex, marital status, oc-
cupation, *département*,<fnref n="10" /> and location of residence (urban/rural/semiurban
areas).

**3.1 Initial sample**

Our observation period runs from October 2020 to March 2024. Our main
initial raw data is a sample of about 300,000 households. The sampling
method ensures that the final sample is representative of the bank. The data
provided pertain to clients meeting the following conditions: clients *engaged*
with the bank (a marketing segmentation criterion of the Bank indicating
that LBP is likely the main bank of the client) and adult clients born after
1923 on one of the following days (January 2nd to 5th, April 1st to 4th, July
1st to 4th, October 1st to 4th). Each month, new clients who meet these
conditions are added to the sample.

<footnote n="9">⁹Savings accounts include *Livret A*, *Livret Jeune*, *Livret de Développement Durable et Solidaire*, *Compte Épargne Logement*, and time deposits</footnote>

<footnote n="10">¹⁰An administrative division similar to a county in the U.S. (*départements* are on average 3.5 times bigger). Mainland France, excluding Corsica and overseas territories, is divided into 94 *départements*.</footnote>
## 3.2 Construction of key variables

We now detail the construction of our key financial variables from this data: disposable income, spending, savings, and financial wealth.

**Disposable income** To construct monthly disposable income, we focus on specific types of account inflows. First, it includes all transfers from organizations that the bank has identified as income.<fnref n="11" /> These inflows encompass wages, pensions, welfare benefits, and unemployment benefits, each of which can be separately identified. Pensions, welfare benefits, and unemployment benefits are identified by the bank based on the name of the issuing organization, while wages are identified as the remaining transfers from organizations labeled as income by the bank. Additionally, disposable income includes all incoming checks. Finally, we subtract identified tax amounts in direct debit transactions from these inflows to obtain disposable income. Note that capital income is ignored.

**Spending** We define total spending as the sum of various outflows. First, we include all outgoing transactions made via debit and credit card (including cash withdrawals) and paper checks. Second, we add direct debits, excluding tax payments and credit refunds to the bank (which are considered savings). This definition excludes outgoing transfers, as our data lack the granularity to differentiate between consumption (e.g., rent payments) and savings (transfers between accounts held in different banks by the same households). This measure is restrictive, and we are likely to miss a portion of spending due to transfers. Typically, rents paid via transfers (and not in cash, checks, or direct debit) are excluded from our definition. Furthermore,

<footnote n="11">¹¹The definition is broad. Inflows from insurance or credit companies are excluded.</footnote>
since March 2022, the bank has provided Merchant Category Codes (MCCs)
for card payments, which categorize merchants depending on the types of
goods and services they mainly sell.<fnref n="12" />

**Savings** We compute savings from the change in monthly balances across all financial accounts (deposit, savings, life insurance, and securities). We add up outflows net of inflows from any account in another bank with identical surnames.<fnref n="13" /> Finally, we include credit refunds (mortgage and consumer loan repayments) and subtract amounts corresponding to new consumer loans.<fnref n="14" /> This measure is restrictive, and we are likely to miss part of the savings due to transfers to financial institutions. We consider our measure a lower bound that excludes partially illiquid savings. Despite these potential limitations, the results of our event study demonstrate that we are able to capture most of the variations following the income shock due to job loss.

**Financial wealth** The financial wealth detained by households corresponds to the sum of the monthly balances over all their bank accounts, which includes both liquid (e.g. deposit and savings accounts) and illiquid (e.g. life insurance and securities) assets.

All outcomes are winsorized below the 2,5 percentile and above the 97,5
percentile, following the literature using bank data (Andersen et al., 2020;
Ganong and Noel, 2019).

<footnote n="12">¹²The categorization is based on the information reported by merchants to the Internal Revenue Service</footnote>

<footnote n="13">¹³The data we access is anonymized, but the bank flags transactions with identical surnames.</footnote>

<footnote n="14">¹⁴We do not subtract amounts corresponding to new mortgage loans: while these reduce financial wealth, they increase housing wealth.</footnote>
## 3.3 Completeness and representativeness issues

Despite numerous comparative advantages of bank account data, two concerns have been raised by the literature as regards external validity of such sources (Baker, 2018): representativeness and completeness. Representativeness issues arise because each bank may target specific customers, who differ from the general population (additionally, households without bank accounts are inherently excluded from the sample). Completeness issues arise because each household may hold assets and means of payment outside their primary bank, leading to only a partial observation of the household's financial situation. Therefore, we utilize several external sources to assess both the representativeness of the bank's clients and the completeness of household financial information.

**Representativeness** To assess representativeness issues, we use the national wealth survey which provides information on household wealth and income and on the banking institutions where deposit accounts are held. In the survey, we consider that households primarily bank at LBP if they hold their deposit account with the highest balance there. We observe in that survey that the distribution of income of households in LBP matches that of the whole population (Table A.1 in the appendix), even though customers at LBP tend to be poorer (median annual income of 26,000 euros versus 32,000).<fnref n="15" /> They also tend to have lower financial wealth (Table A.2 in the appendix).

<footnote n="15">¹⁵There is a one-decile difference between the distribution of the general population and that of the postal bank (D2 in the postal bank is equal to D1 in the general population, D3 to D2 etc).</footnote>
**Completeness** Households may hold accounts in multiple banks, potentially leading to measurement errors in income, consumption, and particularly financial wealth within our dataset. Households in the lowest decile of income maintain 92% of their banking assets in their primary bank, compared to 82% for those in the highest decile (Table A.3 in Appendix).

**Assessing completeness and representativeness issues** Nonetheless, despite these issues, comparisons between statistics derived from bank data and those from representative surveys are reassuring. The distribution of income obtained from transaction data closely matches that from surveys (Figure A.1 in Appendix). Furthermore, average household expenditures and wealth relative to their position in the standard of living distribution in bank data also closely align with those from representative surveys (Figure A.2 and A.3 in Appendix). Spending-to-income ratios appear similar (Figure A.4), decreasing from 1.6 for bottom 10% of income to 0.70 for top 10%. The spending-to-income ratios are, on average, higher in the bank data. This may be partly explained by the fact that LBP customers are, on average, poorer, and by the exclusion of capital income from our definition of income.

Overall, previous comparisons with external sources suggest that representativeness and completeness are not major concerns when tracking the response of income and spending to an unemployment shock using our data. If anything, we may underestimate financial wealth (particularly illiquid assets) and savings, both because LBP customers tend to be poorer and because a portion of financial wealth is held outside the bank. This is confirmed by Table 1, which shows that these customers have lower incomes and financial wealth than average.
## 3.4 Sample selection

Although we do not observe individual employment history in the data, we have access to monthly indicators that inform us whether households have received unemployment benefits (UB) and wages. Therefore, we rely on these indicators to define an unemployment spell. In our main specification, we consider a 12-months observation window: from 6 months before the loss of a job to 6 months after. To avoid mismeasurement issues, we impose three restrictions on our sample: we exclude households 1/ with transfers exceeding €60,000, 2/ with fewer than five outgoing transactions per month, and 3/ with less than €150 of income in the previous quarter. The first restriction is imposed because large financial transactions are unlikely to be related to the unemployment event and make it difficult to isolate saving and spending responses (these operations may be linked to housing purchases). The second and third restrictions aim to exclude customers for whom the bank may incorrectly estimate as their primary bank and whose main source of income is not reflected in the accounts held in the bank.

### 3.4.1 Treatment group : definition of unemployment spells in our data

For identification purposes, our analysis is limited to unemployed persons receiving benefits. A job loss is characterized as a situation where the number of monthly transfers labeled as wages declines by at least one in the household (typically, from 1 to 0 for singles and from 2 to 1 for couples if both members were initially employed and one loses his/her job). We then construct several sub-samples based on the duration of the unemployment spell. We define that a household finds a job in month *m* if the number of wage-labeled transfers increases in month *m* relative to the month of the job
loss. To avoid false positives (households incorrectly identified as losing their
jobs), we impose two additional mild restrictions. First, we limit our analy-
sis to households that received fewer wage-labeled transfers<sup>16</sup> after the shock
and more unemployment benefit transfers. Second, we exclude households
who do not receive the same number of wage transfers each month before the
job loss,<sup>17</sup> thus restricting our sample to households that had some stability
in employment before the spell. The sample size is composed of 6,439 house-
holds. In our primary specification, we concentrate on households that have
been unemployed for a minimum of six months, which encompasses 2,409
households.

3.4.2 Control group

To estimate the impact of job loss on consumption, one might simply compare
the evolution of spending before and after the shock. However, this observed
change may be influenced by confounding factors such as seasonality, eco-
nomic conditions, or specific trends. As underlined by Fagereng, Onshuus,
and Torstensen (2024), “the ideal experiment for investigating consumption
responses after job loss would involve random, unexpected employee termi-
nations”. However, such an experiment is obviously impractical for ethical
reasons. Consequently, researchers have traditionally relied on natural ex-
periments, such as mass layoffs or plant closures, to obtain exogenous shocks
(Schwerdt, 2011). In our dataset, we lack employer-specific information,
preventing us from identifying plant closures or utilizing the mass-layoff ap-
proach. Therefore, our methodology involves constructing a counterfactual
using a control group method as done by Fagereng, Onshuus, and Torstensen

<footnote n="16"><sup>16</sup>In number, not necessarily in amount.</footnote>

<footnote n="17"><sup>17</sup>Except for the month before the job loss because of final and full settlement.</footnote>
(2024) and Gerard and Naritomi (2021).<fnref n="18" />

Our objective is to create a control group whose observable variables are
on average close to those of the treatment group. To do so, every worker
who becomes unemployed is paired with a worker who do not experience
job loss during that period: we restrict our sample to households who have
received the same number of positive transfers labelled wages each month in
the 12-month period but who have not received any unemployment benefits
(UB) or pensions during the period. To render both groups more compa-
rable in terms of observables, we use a propensity score matching (PSM)
approach. Each household in the treatment group is matched to its sibling
in the control group based on the nearest propensity score for each calendar
month of the observation period. The score is computed based on observed
characteristics (financial wealth, age, and the number of adults in the house-
hold) measured in the quarter preceding the observation window. Table 2
confirms that post-PSM samples are more balanced than pre-PSM ones in
these respects, particularly for financial wealth. However, a latent concern
remains: displaced workers and their employed counterparts may exhibit
different trends in income, spending, or savings. To assess the influence of
unobserved factors affecting these trajectories, we examine the pre-trends of
key variables (see section 7).

<footnote n="18">¹⁸Andersen et al. (2020) do not use a control group and just measure the impact on treated, but they provide a robustness checks on a subsample involving mass layoffs and reports results consistent with those from their main sample.</footnote>
<table><caption>Table 1: Summary statistics</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>La Banque Postale<br>sample</th><th>National<br>surveys</th></tr></thead><tbody><tr><td># of observations</td><td>185,543</td><td></td></tr><tr><td></td><td colspan="2"><em>Sample means</em></td></tr><tr><td><u>Financial variable</u></td><td></td><td></td></tr><tr><td>Spending</td><td>2,512</td><td>2,295</td></tr><tr><td>Disposable income</td><td>2,664</td><td>3,138</td></tr><tr><td>Financial Assets</td><td>35,188</td><td>55,372</td></tr><tr><td><em>Liquid financial Assets</em></td><td>22,625</td><td>26,007</td></tr><tr><td><em>Illiquid financial Assets</em></td><td>12,563</td><td>29,364</td></tr><tr><td>Monthly savings</td><td>80</td><td></td></tr><tr><td><u>Demographics</u></td><td></td><td></td></tr><tr><td>Age</td><td>54</td><td>56</td></tr><tr><td>Nb adults</td><td>1.5</td><td>1.79</td></tr></tbody></table>

Notes: Pecuniary amounts: in €.

Sources: La Banque Postale sample, and national surveys: consumer survey (Budget des Familles, 2017, https://www.insee.fr/fr/statistiques/5371205?sommaire=5371304), wealth survey (Histoire de vie et Patrimoine, 2017, authors’ calculations), income survey (Enquêtes sur les Revenus Fiscaux et Sociaux, 2018, https://www.insee.fr/fr/statistiques/5371205?sommaire=5371304) and census (Enquêtes Annuelles de Recensement, 2019, authors’ calculations). Age is estimated on the oldest member in the households.
<table><caption>Table 2: Summary statistics (by treatment status, before and after PSM adjustment)</caption><thead><tr><th rowspan="2"></th><th colspan="2">Unadjusted</th><th colspan="2">Adjusted</th></tr><tr><th>Control</th><th>Treated</th><th>Control</th><th>Treated</th></tr></thead><tbody><tr><td>Age</td><td>48</td><td>39</td><td>39</td><td>39</td></tr><tr><td># of adults</td><td>1.4</td><td>1.2</td><td>1.3</td><td>1.2</td></tr><tr><td>Disposable income</td><td>2,487</td><td>2,055</td><td>2,307</td><td>2,055</td></tr><tr><td>Spending</td><td>2,240</td><td>1,864</td><td>2,065</td><td>1,864</td></tr><tr><td>Savings</td><td>123</td><td>123</td><td>87</td><td>123</td></tr><tr><td>Financial wealth</td><td>30,676</td><td>7,655</td><td>10,753</td><td>7,655</td></tr><tr><td><i>Liquid assets</i></td><td>21,370</td><td>6,715</td><td>9,612</td><td>6,715</td></tr><tr><td><i>Illiquid assets</i></td><td>8,165</td><td>687</td><td>1,105</td><td>687</td></tr></tbody></table>

*Notes*: Pecuniary amounts: in €. Summary statistics on treated and control group before and after propensity score matching. Means estimated the quarter preceding the observation window and all financial variables are winsorised. The winsorisation implies that the average financial wealth is not equal to the sum of liquid and illiquid assets.

*Sources*: La Banque Postale sample, authors' calculations.
# 4 Empirical analysis: estimation method

## Estimating dynamic treatment effect
Our research design leverages an event study around job loss, unemployment spell being the event. We estimate dynamic treatment effects on several outcomes to provide a decomposition of the income shock along several dimensions such as spending and savings. We control for individual ($\alpha_i$) and month ($\gamma_t$) fixed-effects. A two-way fixed effects approach corresponds to the estimation of the following equation:

$$y_{it} = \sum_{h=-6}^{+5} \beta_h 1[e_{it} = h] + \alpha_i + \gamma_t + \epsilon_{it}, \quad (1)$$

where $y_{it}$ are the outcomes variables, $e_{it}$ is event time defined as distance in months to job loss and $\epsilon_{it}$ are idiosyncratic heterogeneity terms. A large recent literature has been dedicated to investigate the biases in the treatment estimates (parameter $\beta$) that could be introduced by such estimation in a setting where all units are not treated at the same time (Borusyak and Jaravel, 2017; De Chaisemartin and d'Haultfoeuille, 2020; Callaway and Sant'Anna, 2021). In the context of event studies, Sun and Abraham (2021) show that “in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods”. They propose an alternative method, defining cohorts by treatment dates and estimating different treatment effects for each cohort. They then estimate the average treatment effect across all cohorts using sample shares as weights. They argue that these weights are more interpretable than those underlying the traditional two-way fixed effects regression. We apply their alternative method to estimate the dynamic treatment effects for all our outcome variables.
The method consists of two steps. In the first step, a two-way fixed effects equation is estimated where time to treatment is interacted with cohorts dummies to recover cohort specific treatment effect:

$$y_{it} = \sum_{c} \sum_{h=-6}^{+5} \delta_{c,h} \mathbf{1}[e_{it} = h] \mathbf{1}[C_i = c] + \alpha_i + \gamma_t + \epsilon_{it}, \quad (2)$$

where $C_i = c$ indicates that units $i$ belongs to cohort $c$, and $\delta_{c,h}$ corresponds to the treatment effect for cohort $c$ in relative period $h$. In a second step, these cohort specific treatment effects are aggregated to obtain the average treatment for the treated (ATT) for each relative period. The average interaction-weighted estimator for a given relative period is then:

$$\hat{\beta}_{h}^{sa} = \sum_{c} \hat{\delta}_{c,h} \hat{s}_{hc} \quad (3)$$

where $\hat{s}_{hc}$ are sample shares. Standard errors are clustered at the household level. We normalize $\beta_{-5} = 0$, hence the reference of our event study is 5 months before the job loss. As a robustness check, we also estimate the event study using the traditional two-way fixed effects estimation, and we find similar results. Hereafter, we denote $\hat{\beta}_{h}^{sa}(Y)$ the average treatment effect for relative period $h$ using this two-step procedure and variable $Y$ as outcome (income, spending, savings, etc.).

## Estimates in differences, levels or in percentage of pre-event income

$\hat{\beta}_{h}^{sa}(Y)$ corresponds to the estimated difference in euros between the level of variable $Y$ $h$ months after the job loss relative to its level 5 months before (the reference month), controlling for trend and seasonality variation using the control group. To obtain average outcomes $Y$ for each months around job loss, we add to each coefficient $\beta$ the average level of the outcome variable
in the treated group in the reference period -5. To express the variation of
all of our outcomes as a percentage of pre-event levels, we divide β̂<sub>h</sub><sup>sa</sup>(Y) by
the average income in the treated group 5 months before job loss. Standard
errors are computed using a bootstrapping procedure with resampling of
individuals, rather than observations, to account for heteroskedasticity and
autocorrelation within observations for the same individual.

5 Results

5.1 Event study analysis

**Decomposing the income shocks** In our primary analysis, we decom-
pose the evolution of earnings around job loss for households that remain
unemployed for at least six consecutive months. The institutional setting
implies that the drop in disposable income due to job loss is only partially
compensated by unemployment benefits (section 2).

Six months after job loss, disposable income is €630 lower than it would
have been if the household had remained employed. With an average dispos-
able income of 2,055 euros five months prior to job loss, this decline repre-
sents a 30% reduction in income. Table 3 presents the estimates of the event
study (equation 3). Figure 1 illustrates the evolution of average outcomes in
levels<fnref n="19" /> around the time of job loss (see Figure B.1 in appendix for the direct
plot of the event study estimates). The reduction in income is attributed to a
decline in labor earnings which are partially compensated by unemployment
benefits. The average replacement rate is 65% (see Appendix C for a detailed

<footnote n="19"><sup>19</sup>To obtain average outcomes *Y* for each months around job loss, we add to each coefficient *β* the average level of the outcome variable in the treated group in the reference period -5, see section 4.</footnote>
discussion of this result).<fnref n="20" /> Following the loss of employment, unemployment benefits increase markedly during the initial three-month period, rising from a negligible level (€27) to an average of €868. This finding is consistent with the National Unemployment Insurance Agency's report, which indicated that the average unemployment benefit was €990 in March 2022 (Unedic, 2023). The delay in unemployment benefits (UB) reaching their maximum level immediately following job loss can be attributed to the automatic deferral of UB for households that receive payment of untaken vacation and extra-legal severance payment, in addition to potential registration delays. Other income sources, such as social security benefits like family allowances, remain stable throughout the period. Labor earnings do not fall to zero after job loss because one household member may remain employed. Furthermore, it is noteworthy that, just before the job loss, the month of the full and final settlement, disposable income increases by €460, representing an average gain of 22%.

In conclusion, three financial impacts are evident around job loss: an initial increase in disposable income one month prior to job loss, a subsequent decline due to wage loss, and a gradual increase over the subsequent three months as delayed unemployment benefits are received.

**The response margins to the income shock** Households may adjust both spending and savings in response to the income shock (Figure 2 shows the results in levels around job loss, Figure 3 in percentage of pre-event income, Table 3 shows the event study estimates, Figure B.2 in the appendix

<footnote n="20">²⁰This figure is slightly higher than the ratio equal to 56% of the coefficient $\hat{\beta}_{h}^{sa}(Y)$ corresponding to spending and income in table 3. This is due to the fact that the beta measure assesses the evolution of wage and unemployment benefits in relation to a hypothetical scenario where households remained employed (and potentially received a wage increase), rather than directly in comparison to the amounts recorded prior to job loss.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 1: Trends in income, wages, unemployment benefits and social security benefits before and after job loss</figcaption></figure>

*Note.* This graph is obtained thanks to the event study estimates around the unemployment spell using the variables income, wages, unemployment benefits and social security benefits as outcomes. The reference period in the regression is 5 months before job loss. We add the average level of each outcome variable 5 months before the shock to obtain the trajectories in levels. The dashed line corresponds to job loss. The horizontal bars around the point estimates correspond to 95% confidence intervals estimated by bootstrapping.

*Source.* La Banque Postale sample, authors’ calculations.
corresponds to the direct plot of the event study ).

After the job loss, liquid savings fall below 0 and households reduce their
consumption consecutive to unemployment. In the very short-run, the first
month following the onset of unemployment, spending exhibits a moderate
decline, reaching a level approximately €40 lower than that observed five
months prior to job loss. This represents a reduction of less than 2% of
the pre-event income. Liquid savings constitute a more substantial margin
of adjustment, diminishing by €700 relative to five months before job loss.
Therefore, in the initial period following job loss, self-insurance accounts
for approximately 80% of the income loss. However, in the medium term,
six months after the commencement of an unemployment spell, spending
tends to decline further, accounting for approximately 46% of the income
loss (equivalent to the MPC observed in the final month).
<figure><img src="image_7.png" /><figcaption>Figure 2: Trends in income, spending and savings to an unemployment spell</figcaption></figure>

*Note.* This graph is obtained thanks to the event study estimates around the unemployment spell using the variables disposable income, savings and spending as outcomes. The reference period in the regression is 5 months before job loss. We add the average level of each outcome variable 5 months before the shock to obtain the trajectories in levels. The dashed line correspond to job loss. The horizontal bars around the point estimates correspond to 95% confidence intervals estimated by bootstrapping. The category others corresponds to financial operations that we were not able to classify such as outgoing bank transfers for example (see section 3.2)

*Source.* La Banque Postale sample, authors’ calculations.
<figure><img src="image_8.png" /><figcaption>Figure 3: Response in income, spending and savings to an unemployment spell in percentage of pre-event income</figcaption></figure>

*Note.* This graph is obtained thanks to the event study estimates around the unemployment spell using the variables disposable income, savings and spending as outcome. The coefficient in the event study are divided by the average level of disposable income 5 months before the shock to interpret parameters in deviation with respect to pre-event income. The dashed line corresponds to job loss. The horizontal bars around the point estimates correspond to 95% confidence intervals estimated by bootstrapping. The category others corresponds to financial operations that we were not able to classify such as outgoing bank transfers for example (see section 3.2).

*Lecture.* 3 months after job loss, spending have decrease by an amount corresponding to 12.5% of the disposable income 5 months before the job loss. *Sources.* La Banque Postale sample, authors’ calculations.
<table><caption>Table 3: Event study: decomposing the response to the unemployment shock</caption><thead><tr><th></th><th>Disposable income<br>(1)</th><th>Wages<br>(2)</th><th>Unemployment Benefits<br>(3)</th><th>Social Security Benefits<br>(4)</th><th>Spending<br>(5)</th><th>Savings<br>(6)</th></tr></thead><tbody><tr><td colspan="7"><em>Time relative to job loss</em></td></tr><tr><td>6 months before</td><td>16.23<br>(12.91)</td><td>0.4928<br>(9.979)</td><td>8.547***<br>(2.814)</td><td>13.09***<br>(3.880)</td><td>-18.01<br>(16.39)</td><td>-9.256<br>(23.03)</td></tr><tr><td>5 months before</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4 months before</td><td>-37.41***<br>(13.34)</td><td>-29.64***<br>(9.538)</td><td>-3.143<br>(2.444)</td><td>1.532<br>(3.856)</td><td>-10.76<br>(16.06)</td><td>-66.36***<br>(23.67)</td></tr><tr><td>3 months before</td><td>-39.37***<br>(14.15)</td><td>-38.73***<br>(10.01)</td><td>-3.251<br>(2.691)</td><td>-4.895<br>(4.277)</td><td>-18.52<br>(16.76)</td><td>-27.31<br>(22.68)</td></tr><tr><td>2 months before</td><td>40.58**<br>(17.23)</td><td>21.37<br>(13.78)</td><td>4.905<br>(3.147)</td><td>2.543<br>(4.799)</td><td>20.06<br>(18.14)</td><td>-6.768<br>(23.92)</td></tr><tr><td>1 month before</td><td>462.5***<br>(26.65)</td><td>312.1***<br>(22.16)</td><td>38.73***<br>(4.808)</td><td>17.95***<br>(5.385)</td><td>95.26***<br>(19.78)</td><td>312.7***<br>(28.92)</td></tr><tr><td>Job loss month</td><td>-863.1***<br>(21.70)</td><td>-1,382.0***<br>(13.75)</td><td>302.7***<br>(10.08)</td><td>30.54***<br>(5.739)</td><td>-37.09*<br>(20.80)</td><td>-691.8***<br>(28.20)</td></tr><tr><td>1 month after</td><td>-647.6***<br>(20.41)</td><td>-1,433.1***<br>(13.79)</td><td>595.9***<br>(10.63)</td><td>43.04***<br>(6.209)</td><td>-191.8***<br>(20.30)</td><td>-388.3***<br>(26.20)</td></tr><tr><td>2 months after</td><td>-573.4***<br>(18.86)</td><td>-1,464.8***<br>(14.07)</td><td>777.0***<br>(9.466)</td><td>50.77***<br>(6.633)</td><td>-205.9***<br>(20.48)</td><td>-284.3***<br>(24.53)</td></tr><tr><td>3 months after</td><td>-556.9***<br>(19.11)</td><td>-1,485.6***<br>(14.38)</td><td>833.4***<br>(8.844)</td><td>45.54***<br>(7.037)</td><td>-273.0***<br>(20.33)</td><td>-208.8***<br>(24.82)</td></tr><tr><td>4 months after</td><td>-578.9***<br>(18.81)</td><td>-1,493.1***<br>(14.70)</td><td>849.3***<br>(8.742)</td><td>14.42**<br>(6.427)</td><td>-297.8***<br>(20.81)</td><td>-202.7***<br>(25.29)</td></tr><tr><td>5 months after</td><td>-628.9***<br>(18.56)</td><td>-1,493.3***<br>(14.85)</td><td>842.6***<br>(9.016)</td><td>-17.14***<br>(6.485)</td><td>-283.8***<br>(22.04)</td><td>-240.3***<br>(25.69)</td></tr><tr><td colspan="7"><em>Fixed-effects</em></td></tr><tr><td>Household</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Calendar month</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td colspan="7"><em>Fit statistics</em></td></tr><tr><td>Observations</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td></tr><tr><td>R<sup>2</sup></td><td>0.71997</td><td>0.86167</td><td>0.76613</td><td>0.80665</td><td>0.67684</td><td>0.17809</td></tr><tr><td>Within R<sup>2</sup></td><td>0.15949</td><td>0.51325</td><td>0.52844</td><td>0.02274</td><td>0.02464</td><td>0.04749</td></tr></tbody></table>

Note. Event study estimates around the unemployment spell using the variables disposable income, wage, unemployment benefits, social security benefits, savings, and spending as outcomes. The reference period in the regression is 5 months before job loss. The average values of the outcomes 5 months before the shock are: €2,054 for disposable income, 1,686 for wage, €27 unemployment benefits, €282 for social security benefits, €1,907 for spending and €127 for savings Clustered (ID) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Source. La Banque Postale sample.
## 5.2 The Marginal Propensity to Consume during the period of unemployment

**Estimation and results** The marginal propensity to consume encapsulates households' responses to income fluctuations. It quantifies the change in consumption, measured in euros, corresponding to a one-euro change in income, reflecting either an increase or decrease. We estimate it for each relative period $h$ as the ratio of the coefficient relative to consumption over the ratio of the coefficient relative to income:

$$MPC_h = \frac{\hat{\beta}_h^{sa}(\text{Spendings})}{\hat{\beta}_h^{sa}(\text{Disposable income})}$$

Standard errors are obtained by bootstrap resampling of households.

The marginal propensity to consume (MPC) is 0.20 for the initial positive shock resulting from the full and final settlement payment. In the month of job loss when income decreases, it is relatively low, at 0.05 (not statistically significant at 5%). Subsequently, there is an increase to 0.46 six months later (Figure 4). The mean MPC over the entire unemployment period is 0.36. This pattern indicates that during the initial months of unemployment, households do not reduce spending but instead draw on their liquid savings to mitigate the impact of the loss of income. As time progresses, they reduce their spending further. Consequently, spending drops by 2% in the month of job loss and by 15% six months later (Figure 5).

**Interpretation** Households reduce consumption during an unemployment spell and do not rely on private savings to entirely smooth the income shock. This phenomenon can be attributed to liquidity constraints: households may have been unable to save sufficiently in the past and cannot (or choose not
<figure><img src="image_9.png" /><figcaption>Figure 4: Monthly marginal propensity to consume</figcaption></figure>

*Notes*: Marginal propensity to consume are computed for each month around job loss thanks to the event study estimates around the unemployment spell. MPC is the ratio of the spending estimates over disposable income estimates. The dashed line correspond to job loss. The horizontal bars correspond to 95% confidence intervals estimated by bootstrapping. MPCs are calculated from 1 month before job loss (before income does not vary, it is not possible to identify the effect).

*Sources:* La Banque Postale sample, authors’ calculations.
<figure><img src="image_10.png" /><figcaption>Figure 5: Variation in spending relative to spending before job loss

*Notes.* Variation of spending relative to the level of spending 5 months before the job loss.

*Sources:* La Banque Postale sample, authors’ calculations.</figcaption></figure>
to) borrow. This is corroborated by the fact that the marginal propensity to consume (MPC) is larger for households with low liquidity (see Section 6 and Figure D.2a). The gradual decrease in consumption over the duration of the unemployment spell could be explained by households’ overoptimistic expectations of quickly finding a job (Spinnewijn, 2015). Households may not reduce consumption sufficiently in the initial months, believing they will secure employment soon. Additionally, this behavior could be attributed to present-biased preferences, where agents prioritize current consumption over future consumption (Ganong and Noel, 2019). The gradual decrease could also be explained by obligatory expenses that require time to be reduced. Direct debit payment (including energy and phone subscription) demonstrate this pattern (Table E.4 in appendix), yet are too small to entirely explain the slow decrease in consumption.

The observation that consumption increases in the month when full set-
tlement payments are received lends support to present-biased behavioral
explanations. In Brazil, Gerard and Naritomi (2021) also find an increase in
consumption before job loss (positive MPC) due to severance payments de-
spite the following decrease in income afterwards. This may appear counter-
intuitive, as one might expect households to save the money received from
severance payments in order to maintain their consumption levels during the
period of unemployment. However, these authors show that such results can
be rationalized by a model of present-biased workers. This highlights the im-
portance of the timing of payments in mitigating the welfare loss associated
with unemployment.

The decline in consumption can be attributed to the necessity of adjust-
ing spending in accordance with income levels. However, this can also be ex-
plained by changes in lifestyle habits. For example, households are no longer
required to commute to work, which reduces the consumption of gasoline, and
they may limit their dining out. In a different context, Aguiar and Hurst
(2005) suggest that the decline in spending at retirement may be attributed
to an increase in home production. The decomposition of the reduction in
consumption into various categories of purchases, including groceries, fuel,
pharmacies, and other leisure expenditures, reveals that the reduction in
spending is particularly pronounced for goods that could complement work,
such as fuel and restaurant expenses (Figure D.3 in Appendix).

# 6 Additional results

## 6.1 Heterogeneity along duration of unemployment

This section examines the differential impact based on the duration of con-
secutive months of unemployment. In particular, we examine the income and
expenditure trajectories of households according to the length of time it took
to secure new employment following unemployment spells. The probability
of finding a job declines as the duration of unemployment increases. The
job hazard rate during this period exhibits a decreasing pattern from 0.29 to
0.16 (Figure D.4 in Appendix). It might be consistent with observed and un-
observed heterogeneity (Paserman, 2008), duration dependence (Kroft and
Notowidigdo, 2016), or reference dependence (DellaVigna et al., 2017).

In the month households find employment, disposable income increases,
reaching a level higher than before the initial unemployment spell (Figure 6).
This phenomenon can be attributed to the fact that, during this month,
households receive both wage and unemployment benefits. The subsequent
months, disposable income remains higher than during the unemployment
spell but lower than before the spell. This may be due to two factors: first,
households may secure new jobs at lower wages with no more UB; second, some households return to unemployment.

For households unemployed for more than one month, the initial months of the spell show adjustments primarily through decreased savings with a moderate reaction in spending. The longer the unemployment spell lasts, the more pronounced the spending response becomes.
<figure><img src="image_11.png" /><figcaption>Figure 6: Income, spending, and savings response to an unemployment spell by duration of unemployment</figcaption></figure>

*Notes*: Spells along number of consecutive months unemployed. This graph is obtained thanks to the event study estimates around the unemployment spell using the variables disposable income, savings and spending as outcome. The coefficient in the event study are divided by the average level of disposable income 5 months before the shock to interpret parameters in deviation (in percentage) with respect to pre-event income. The dashed line correspond to job loss.

*Sources:* La Banque Postale sample, authors’ calculations.
## 6.2 Heterogeneity of MPC across households characteristics

We next estimate the marginal propensity to consume (MPC) out of the overall income shock along observable characteristics of the households. The computation relies on the event study estimates:

$$MPC = \frac{\sum_{h \in [0,5]} \hat{\beta}_h^{sa}(\text{Spending})/6}{\sum_{h \in [0,5]} \hat{\beta}_h^{sa}(\text{Disposable income})/6} \quad (4)$$

These MPCs show no much variation with respect to household size, age, or initial income (Figures D.1a and D.1b in the appendix and D.2c). However, they exhibit significant heterogeneity concerning liquidity and financial wealth (Figures D.2a and D.2b). MPC ranges from 0.56 in the bottom 25% of liquidity holders to 0.19 in the top 25%.

To estimate the impact of each characteristic on average MPC while holding all other factors constant, we perform the following regression on the treatment group:

$$\Delta \text{Spending}_{it} = \sum_{k} \beta_{1}(X_{it}^{k}) \Delta \text{Disposable income}_{it} + \sum_{k} \beta_{0}(X_{it}^{k}) + \mu_{t} + \varepsilon_{it} \quad (5)$$

where the Δ operator corresponds to the difference between the average value of the outcome in months posterior to the job loss and months before. β(X) correspond to the estimated MPC which we allow to vary based on different explanatory variables through interactions. μ_t correspond to calendar month fixed effects. Liquidity is a crucial explanatory factor for the reduction in spending following a decrease in income (Table D.1 in the appendix).
# 7 Robustness

## 7.1 Threats to identification

The identifying assumptions of the dynamic treatment effects are (i) parallel trends and (ii) no anticipation of the treatment *a* months before the job loss, *a* is the anticipation horizon.<fnref n="21" />

The first assumption posits that, in the absence of treatment, outcomes would have evolved similarly for both “treated” and “control” groups. By definition, this assumption cannot be tested for months following the treatment. Nevertheless, the similar evolution of outcomes prior to the shock is reassuring. Evolution of income, spending and savings shows no clear different pre-trend from 6 to 2 months before event, i.e. before the beginning of an unemployment spell (Figure 1 and 2 for event study estimates and Figure E.1 for raw data). The coefficients of the event study regression (Table 3), from 6 months before the shock to 2 months prior, are either statistically insignificant or small in magnitude compared to the shock. If anything, it suggests that the disposable income of the treated group is slightly less dynamic than that of the control group, leading to a slight overestimation of the income shock. Reassuringly, our estimates without controls (Table E.2) yield similar results ruling out a subsequent major bias due to controls evolving with a different trend. One factor that could imply a violation of the common trend hypothesis is that the control group consists of households that have been employed for at least 12 consecutive months. Households in the control group are thus by definition stable in the labor market and may therefore experience more favorable wage trends than the rest of the population. In this respect, however, it is reassuring that the pre-trends are similar for the

<footnote n="21">²¹For more details as regards identification of two-way fixed effects models used in event study designs, see Sun and Abraham (2021).</footnote>
control and treatment groups, and that we do not observe any change in
trends over the period for the control group.

The second assumption implies that individuals do not anticipate the
treatment by time $t + a$, thereby restricting their foresight. In practice, we
set $a = 5$ months before job loss, thereby assuming away anticipation effects 5
months prior to this event. In our study, job loss is fixed at month 0, but the
effects of treatment begin, at a minimum, one month before the job loss with
the perception of full and final settlement. We have not observed any change
in trends before this, which rules out the presence of major anticipation
effects.

## 7.2 Parametric assumptions

Estimates have been tested for robustness across different specifications. In addition to the Sun-Abraham regressions, a traditional two-way fixed effects regression and a regression without controls or time fixed effects was conducted (Tables E.1 and E.2). The results are consistent across these methods, but adjustments are smaller without any controls, this can be explained by the slightly increasing trends, partially explained at least by inflation, in all outcome variables for treated and control group.

## 7.3 Generalisation of the results

It is essential to address the generalizability of our findings. The trajec-
tory estimates presented in this study are derived from data collected from
a single banking institution in a single country over a three-year period. In
Section 3, we evaluated the representativeness of our data, and comparisons
with other representative national surveys yielded encouraging results. How-
ever, we have to acknowledge that our findings are contingent upon a specific
national context in years following the COVID crisis (with lockdowns) and unemployment benefits reforms in years 2019-2021. To test the robustness of our results, we excluded the period prior to 2021 from our sample and our findings are robust to this exclusion (Table E.3).

Moreover, our analysis is limited to unemployed individuals who are eligible for unemployment benefits. This restriction implies that our focus is on households that have experienced some degree of employment stability prior to job loss. Adjustments in spending and savings behavior for unemployed individuals who are not eligible for these benefits may differ.

Finally, in our main specification, MPCs are calculated for people who have been unemployed for 6 consecutive months. They therefore do not necessarily correspond to the average MPC of the population, due to the dynamic selection of the sample. The greater or lesser adjustment of consumption when losing a job may be directly related to the efforts made to get out of unemployment. It is possible that households that reduce their consumption more will remain unemployed for a shorter time and thus drop out of our main sample of interest. In this respect, the fact that the MPCs in the first months of unemployment are similar depending on the duration of unemployment tends to reassure us about the generalizability of the results (see Figure 6).

## 7.4 Other sources of adjustments

This study focuses on two primary sources of adjustment: savings and spending. However, other adjustment sources may play a significant role. Among these unobserved adjustment sources, potential candidates include spousal wages, consumer credit, or intra-family transfers. These sources of adjustment are not directly observable: it is not possible to identify which house-
hold member receives a wage-labeled transfer, distinguish transfers and direct debits to credit institutions (as only credit made to the bank is observed), or observe all intra-family inflows and outflows.

However, these margins appear, on average, to be of lesser magnitude than the spending and savings adjustments we measure, aligning with the findings of Andersen et al. (2020) in Denmark. First, there is no observable in wages following the income shock for households with one member remaining unemployed (Figure 1), which suggests that the other member of the couple does not increase their labor supply. Furthermore, marginal propensity to consume (MPC) is comparable between singles and couples (Figure D.1a). Second, there is no evidence of an increase in consumer credit made to the bank. Third, there is no evidence of significant changes in private transfers, which include intra-family transfers, around the period of unemployment (Table E.4). Additionally, residual adjustments (categorized as “Other” in Figures 2 and 3) do not show substantial evolution.
# 8 Conclusion

This study examines the financial adjustments of French households in response to job loss using high-frequency bank account data. Our findings indicate that unemployment benefits partially offset the income loss, with 36% compensated through reduced consumption and the remainder through decreased liquid savings. The consumption reduction intensifies with prolonged unemployment, and households with lower liquidity exhibit higher marginal propensities to consume.

Our results highlight the critical role of liquid savings in financial resilience during unemployment. Policies enhancing household savings and providing targeted support to low-liquidity households could mitigate the adverse effects of income shocks.

On the empirical side, future research could try to estimate the impact of unemployment for ineligible households (while we focus only on the compensated) or try to describe the individual heterogeneity in the responses to shocks. On the theoretical side, future research could try to model the behavior of households in line with our empirical results. This would make it possible to simulate the welfare effects of different public policies.

# References

* Aguiar, M., and E. Hurst. 2005. “Consumption versus expenditure.” *Journal of political Economy* 113:919–948.

* Andersen, A.L., E.T. Hansen, N. Johannesen, and A. Sheridan. 2020. “Consumer responses to the COVID-19 crisis: Evidence from bank account transaction data.” Available at SSRN 3609814.
Andersen, A.L., A.S. Jensen, N. Johannesen, C.T. Kreiner, S. Leth-Petersen, and A. Sheridan. 2023. “How do households respond to job loss? lessons from multiple high-frequency datasets.” *American Economic Journal: Applied Economics* 15:1–29.

Baily, M.N. 1978. "Some aspects of optimal unemployment insurance." *Journal of public Economics* 10:379-402.

Baker, S.R. 2018. “Debt and the response to household income shocks: Validation and application of linked financial account data.” *Journal of Political Economy* 126:1504–1557.

Borusyak, K., and X. Jaravel. 2017. “Revisiting event study designs.” Working paper.

Browning, M., and T.F. Crossley. 2001. “Unemployment insurance benefit levels and consumption changes.” *Journal of Public Economics* 80:1–23.

Callaway, B., and P.H. Sant'Anna. 2021. “Difference-in-differences with multiple time periods.” *Journal of Econometrics* 225:200–230.

Chetty, R. 2006. “A general formula for the optimal level of social insurance.” *Journal of Public Economics* 90:1879–1901.

—. 2008. “Moral hazard versus liquidity and optimal unemployment insurance.” *Journal of political Economy* 116:173–234.

Clark, A.E., E. Diener, Y. Georgellis, and R.E. Lucas. 2008. “Lags and leads in life satisfaction: A test of the baseline hypothesis.” *The Economic Journal* 118:F222–F243.
De Chaisemartin, C., and X. d'Haultfoeuille. 2020. "Two-way fixed effects estimators with heterogeneous treatment effects." *American economic review* 110:2964–2996.

DellaVigna, S., A. Lindner, B. Reizer, and J.F. Schmieder. 2017. "Reference-dependent job search: Evidence from Hungary." *The Quarterly Journal of Economics* 132:1969–2018.

Engen, E.M., and J. Gruber. 2001. "Unemployment insurance and precautionary saving." *Journal of monetary Economics* 47:545–579.

Fagereng, A., H. Onshuus, and K.N. Torstensen. 2024. "The consumption expenditure response to unemployment: Evidence from Norwegian households." *Journal of Monetary Economics*, pp. 103578.

Ganong, P., and P. Noel. 2019. "Consumer spending during unemployment: Positive and normative implications." *The American Economic Review* 109:2383–2424.

Gerard, F., and J. Naritomi. 2021. "Job Displacement Insurance and (the Lack of) Consumption-Smoothing." *The American Economic Review* 111:899–942.

Gruber, J. 1997. "The consumption smoothing benefits of unemployment insurance." *The American Economic Review* 87:192.

Hendren, N. 2017. "Knowledge of future job loss and implications for unemployment insurance." *American Economic Review* 107:1778–1823.

Kaplan, G., G.L. Violante, and J. Weidner. 2014. "The Wealthy Hand-to-Mouth." *Brookings Papers on economic activity*, pp. 77–138.
Kolsrud, J., C. Landais, P. Nilsson, and J. Spinnewijn. 2018. "The optimal timing of unemployment benefits: Theory and evidence from Sweden." *American Economic Review* 108:985–1033.

Kroft, K., and M.J. Notowidigdo. 2016. "Should unemployment insurance vary with the unemployment rate? Theory and evidence." *The Review of Economic Studies* 83:1092–1124.

Landais, C., and J. Spinnewijn. 2021. "The value of unemployment insurance." *The Review of Economic Studies* 88:3041–3085.

Paserman, M.D. 2008. "Job search and hyperbolic discounting: Structural estimation and policy evaluation." *The Economic Journal* 118:1418–1452.

Schwerdt, G. 2011. "Labor turnover before plant closure: "Leaving the sinking ship" vs. "Captain throwing ballast overboard"." *Labour Economics* 18:93–101.

Spinnewijn, J. 2015. "Unemployed but optimistic: Optimal insurance design with biased beliefs." *Journal of the European Economic Association* 13:130–167.

Sun, L., and S. Abraham. 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects." *Journal of economics* 225:175–199.

Unedic. 2023. "Dossier de synthèse, Assurance chomage."
# Appendix

## A Representativeness and completeness

The following figures and tables display information that enables us to assess the importance of representativeness and completeness issues.

### A.1 Figures

<figure><img src="image_12.png" /><figcaption>Figure A.1: Income deciles (La Banque Postale sample vs. national income survey, enquêtes Revenus fiscaux et sociaux 2018)</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure A.2: Household liquid assets, by group of income (La Banque Postale sample vs. French wealth survey, *Histoire de vie et Patrimoine* 2017)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Figure A.3: Household spending, by group of income (La Banque Postale sample vs. consumption survey, *Budget des Familles* 2017)</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure A.4: Household spending-to-income ratio, by group of income (La Banque Postale sample vs. consumption survey, *Budget des Familles* 2017)</figcaption></figure>
## A.2 Tables

<table><caption>Table A.1: Income deciles (estimated from wealth national survey)</caption><thead><tr><th></th><th>All banks</th><th>La Banque Postale</th></tr></thead><tbody><tr><th scope="row">Income group</th><td></td><td></td></tr><tr><th scope="row">D1</th><td>14,500 (229)</td><td>11,930 (881)</td></tr><tr><th scope="row">D2</th><td>18,570 (213)</td><td>15,440 (418)</td></tr><tr><th scope="row">D3</th><td>22,530 (234)</td><td>18,790 (696)</td></tr><tr><th scope="row">D4</th><td>26,580 (330)</td><td>22,500 (779)</td></tr><tr><th scope="row">D5</th><td>31,580 (383)</td><td>25,890 (692)</td></tr><tr><th scope="row">D6</th><td>37,390 (375)</td><td>30,310 (827)</td></tr><tr><th scope="row">D7</th><td>43,790 (386)</td><td>36,340 (1,136)</td></tr><tr><th scope="row">D8</th><td>52,400 (448)</td><td>44,960 (1,216)</td></tr><tr><th scope="row">D9</th><td>66,370 (757)</td><td>57,260 (1,745)</td></tr></tbody></table>

*Notes.* Deciles: in €. Standard deviations (in parentheses). In the national survey, the median income is 31.670 in the sample and 25.890 for the households who primarily bank at *La Banque Postale*. Sample is restricted to households for which we can determine a primary bank.

*Source.* French national wealth survey (*Histoire de vie et Patrimoine*, 2017).
<table><caption>Table A.2: Financial wealth deciles (wealth national survey)</caption><thead><tr><th></th><th>All banks</th><th>La Banque Postale</th></tr></thead><tbody><tr><th scope="row">Income group</th><td></td><td></td></tr><tr><th scope="row">D1</th><td>350 (33)</td><td>150 (30)</td></tr><tr><th scope="row">D2</th><td>1,033 (50)</td><td>475 (56)</td></tr><tr><th scope="row">D3</th><td>2,674 (152)</td><td>1,000 (105)</td></tr><tr><th scope="row">D4</th><td>5,640 (210)</td><td>2,200 (264)</td></tr><tr><th scope="row">D5</th><td>10,822 (351)</td><td>5,420 (471)</td></tr><tr><th scope="row">D6</th><td>18,948 (699)</td><td>10,220 (1,128)</td></tr><tr><th scope="row">D7</th><td>31,723 (865)</td><td>20,650 (2,282)</td></tr><tr><th scope="row">D8</th><td>56,090 (1657)</td><td>37,330 (2,785)</td></tr><tr><th scope="row">D9</th><td>116,119 (3205)</td><td>88,634 (7,045)</td></tr></tbody></table>

*Notes.* Deciles: in €. Standard deviations (in parentheses). In the national survey, the median financial wealth is 10.822 in the sample and 5.420 for the households who primarily bank at La Banque Postale. Sample is restricted to households for which we can determine a primary bank.

*Source.* *Histoire de vie et Patrimoine*, French wealth survey (2017).
<table><caption>Table A.3: Share of bank assets in their main bank</caption><thead><tr><th></th><th>Share of bank asset in the main bank</th></tr></thead><tbody><tr><td>Constant</td><td>0.90 (0.01)</td></tr><tr><td>Income deciles group</td><td></td></tr><tr><td>D1</td><td>Ref.</td></tr><tr><td>D2</td><td>-0.00 (0.01)</td></tr><tr><td>D3</td><td>-0.02 (0.02)</td></tr><tr><td>D4</td><td>-0.05 (0.02)</td></tr><tr><td>D5</td><td>-0.06 (0.02)</td></tr><tr><td>D6</td><td>-0.09 (0.02)</td></tr><tr><td>D7</td><td>-0.07 (0.02)</td></tr><tr><td>D8</td><td>-0.10 (0.02)</td></tr><tr><td>D9</td><td>-0.10 (0.02)</td></tr><tr><td>D10</td><td>-0.15 (0.02)</td></tr></tbody></table>

*Note.* Households in the bottom 10% of income detain 90% of their bank assets in their main bank, against 78% for those in the top 10%. On average, households detain 88.4% of their financial assets in their main bank. These results are obtained by regressing the household's share of bank assets in the main bank on a categorical variable that determines the household's income decile.

*Source.* *Histoire de vie et Patrimoine* French wealth survey (2017).
B Event study graph

This section includes the plots of the event study estimates. It corresponds
to the plot of the results in table 3.
<figure><img src="image_16.png" /><figcaption>Figure B.1: Event study estimates for various outcomes: disposable income, wage, unemployment benefits and social security benefits</figcaption></figure>

*Note.* This graph is obtained thanks to the event study estimates around the unemployment spell using the variables income, wages, unemployment benefits and social security benefits as outcomes. The reference period in the regression is 5 months before job loss. The dashed line corresponds to job loss. The horizontal bars around the point estimates correspond to 95% confidence intervals estimated by bootstrapping.

*Source.* La Banque Postale sample, authors’ calculations.
<figure><img src="image_17.png" /><figcaption>Figure B.2: Event study estimates for various outcomes: disposable income, spending and savings</figcaption></figure>

*Note.* This graph is obtained thanks to the event study estimates around the unemployment spell using the variables disposable income, spending and savings as outcomes. The reference period in the regression is 5 months before job loss. The dashed line corresponds to job loss. The horizontal bars around the point estimates correspond to 95% confidence intervals estimated by bootstrapping.

*Source.* La Banque Postale sample, authors’ calculations.
C Replacement rate

The adjustment of spending depends on the replacement rate, i.e., on the
proportion of the wage loss that is compensated by unemployment benefits.
In our main sample, the replacement rate is 65%. This rate is measured as
the ratio of the average gain in unemployment benefits to the average loss in
wages. The average gain and losses are measured by the evolution of each
outcome variable from the period 6 to 2 months before job loss to 3 to 5
months after. The period from 1 month before the job loss to 2 months after
is ignored because it encompasses final and full settlement as well as the
delay in obtaining unemployment benefits. Figure C.1a, Figure C.1b, and
Figure C.1c show the distributions of the wage loss during the unemployment
spell, of unemployment benefits and of the replacement rate, respectively.

This rate is lower than the 71% net replacement rate calculated by un-
employment agencies.<fnref n="22" /> This discrepancy can be attributed to the fact that
the rate was calculated before recent reforms, which tend to decrease the
replacement rate. Previously, the sum of wages was divided by working days
to determine the reference wage; now, it includes all calendar days since the
start of the contract within the affiliation period. Additionally, our replace-
ment rate is calculated based on monthly wages prior to job loss, whereas
the national agency calculates its reference wage as the ratio of total wages
earned 24 to 36 months before job loss to the number of calendar days since
the beginning of the first employment in that period. Consequently, if house-
holds experienced wage increases during the two to three years before job loss
or had months without employment, our estimated replacement rate would
be lower than that of the national agency. Moreover, the agency's replace-

<footnote n="22">²²This figure can be found on their website https://www.unedic.org/publications/le-montant-de-lallocation-chomage.</footnote>
<figure><img src="image_18.png" /><figcaption>Figure C.1: Distribution around unemployment</figcaption></figure>

*Note.* First and second panel show the distribution of wage loss and the increase in unemployment benefits due to job loss. Last panel shows the distribution of the replacement rate.

*Source.* La Banque Postale sample, authors’ calculations.

ment rate calculation excludes various bonuses that we include in our wage
measure, such as reimbursement of business expenses, insecurity bonus, and
holiday pay.
D Additional results
D.1 Heterogeneity in MPC

<figure><img src="image_19.png" /><figcaption>Figure D.1: Heterogeneity along age and family status</figcaption></figure>

*Note.* Marginal propensity to consume out of a decrease in income during an unemployment spell.

*Sources:* La Banque Postale sample, authors’ calculations.
<figure><img src="image_20.png" /><figcaption>Figure D.2: Heterogeneity along some observed characteristics</figcaption></figure>

*Note.* Marginal propensity to consume out of a decrease in income during an unemployment spell. Sample is divided in four groups of equal size depending on the level of liquidity assets held in the bank divided by the number of adults in the household in panel D.2a, depending on the level of financial wealth in panel D.2b and on the level of income in panel D.2c.

*Source.* La Banque Postale sample, authors’ calculations.
<table><caption>Table D.1: Heterogeneity in the spending response</caption><thead><tr><th>Dependent Variable:</th><th colspan="2">Δ spending</th></tr><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>Δ disposable income</td><td>0.3803***<br>(0.0128)</td><td>0.4820***<br>(0.0469)</td></tr><tr><td><u>Family structure</u></td><td></td><td></td></tr><tr><td>Δ disposable income × Couple</td><td></td><td>Ref</td></tr><tr><td>Δ disposable income × Single</td><td></td><td>-0.0388<br>(0.0292)</td></tr><tr><td><u>Age</u></td><td></td><td></td></tr><tr><td>Δ disposable income × Above 50 yrs old</td><td></td><td>-0.0496<br>(0.0311)</td></tr><tr><td>Δ disposable income × 30 to 50 yrs old</td><td></td><td>Ref</td></tr><tr><td>Δ disposable income × Under 30 yrs old</td><td></td><td>-0.0376<br>(0.0350)</td></tr><tr><td><u>Income group</u></td><td></td><td></td></tr><tr><td>Δ disposable income × 1st group</td><td></td><td>Ref</td></tr><tr><td>Δ disposable income × 2nd group</td><td></td><td>0.1002**<br>(0.0439)</td></tr><tr><td>Δ disposable income × 3rd group</td><td></td><td>0.0564<br>(0.0422)</td></tr><tr><td>Δ disposable income × 4th group</td><td></td><td>0.0583<br>(0.0391)</td></tr><tr><td><u>Liquid assets group</u></td><td></td><td></td></tr><tr><td>Δ disposable income × 1st group</td><td></td><td>Ref</td></tr><tr><td>Δ disposable income × 2nd group</td><td></td><td>-0.1011**<br>(0.0438)</td></tr><tr><td>Δ disposable income × 3rd group</td><td></td><td>-0.2054***<br>(0.0427)</td></tr><tr><td>Δ disposable income × 4th group</td><td></td><td>-0.2444***<br>(0.0401)</td></tr><tr><td><u>Controls</u></td><td>✓</td><td>✓</td></tr><tr><td><u>Calendar month fixed effects</u></td><td>✓</td><td>✓</td></tr><tr><td><u>Fit statistics</u></td><td></td><td></td></tr><tr><td>Observations</td><td>4,781</td><td>4,781</td></tr><tr><td>R<sup>2</sup></td><td>0.15495</td><td>0.19855</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.15478</td><td>0.19535</td></tr></tbody></table>

IID standard-errors in parentheses

Signif. Codes: ***: 0.01, **: 0.05, *: 0.1
## D.2 Heterogeneity in the spending response

<figure><img src="image_21.png" /><figcaption>Figure D.3: Heterogeneity of spending response (by category of purchase)
*Note.* This graph is obtained thanks to the event study estimates around the unemployment spell using cards payments on various categories of merchants as outcomes (using MCC classifications). The reference period in the regression is 5 months before job loss. The dashed line corresponds to job loss. The horizontal bars around the point estimates correspond to 95% confidence intervals estimated by bootstrapping.</figcaption></figure>

*Sources:* La Banque Postale sample, authors’ calculations.
## D.3 Descriptive statistics according to the length of the unemployment spell

<table><caption>Table D.2: Summary Statistics</caption><thead><tr><th>Nb consecutive months unemployed</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>11</th></tr></thead><tbody><tr><td>Disposable income</td><td>2178</td><td>2208</td><td>2092</td><td>2145</td><td>2211</td><td>1976</td><td>1893</td></tr><tr><td>Savings</td><td>88</td><td>84</td><td>63</td><td>72</td><td>123</td><td>35</td><td>18</td></tr><tr><td>Spending</td><td>2077</td><td>2071</td><td>1980</td><td>2039</td><td>2074</td><td>1951</td><td>1953</td></tr><tr><td>Financial wealth</td><td>11355</td><td>11793</td><td>9479</td><td>10860</td><td>10945</td><td>9082</td><td>9303</td></tr><tr><td>Liquid assets</td><td>9659</td><td>9674</td><td>8385</td><td>8721</td><td>8602</td><td>7489</td><td>6966</td></tr><tr><td>Illiquid assets</td><td>1695</td><td>2119</td><td>1095</td><td>2139</td><td>2342</td><td>1593</td><td>2337</td></tr><tr><td>Age</td><td>39</td><td>39</td><td>38</td><td>37</td><td>39</td><td>39</td><td>40</td></tr><tr><td>Nb adults</td><td>1.3</td><td>1.3</td><td>1.2</td><td>1.3</td><td>1.3</td><td>1.2</td><td>1.3</td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>... Women</td><td>46%</td><td>46%</td><td>44%</td><td>45%</td><td>44%</td><td>53%</td><td>61%</td></tr><tr><td>... Men</td><td>54%</td><td>54%</td><td>56%</td><td>55%</td><td>56%</td><td>47%</td><td>39%</td></tr></tbody></table>

Notes: Pecuniary amounts: in €.

Sources: LBP data, statistics are calculated 7 months before the unemployment spell. For households with several members, socio-demographics characteristics correspond to the ones of the older individual in the household.
D.4 Job finding hazard rate

<figure><img src="image_22.png" /><figcaption>Figure D.4: Job finding hazard rate</figcaption></figure>

*Note.* This graph illustrates the proportion of households returning to employment over time, conditional on the duration of their unemployment spell.

*Lecture.* 29% percent of households that experienced job loss are re-employed one month later. Among those who remained unemployed after the first month, 23% found employment two months after the initial unemployment period.

*Source.* La Banque Postale sample, authors’ calculations.
# E Robustness

Robustness tests of the parametric assumption

## E.1 Figures

<figure><img src="image_23.png" /><figcaption>Figure E.1: Control and treatment group</figcaption></figure>

*Note.* Evolution of main outcome for control and treatment group around unemployment spell. For the control group, the month for job loss is fictitious (drawn randomly).

*Source.* La Banque Postale sample, authors’ calculations.
## E.2 Regression tables

<table><caption>Table E.1: Event study: decomposing the response to the unemployment shock, two-way fixed effects</caption><thead><tr><th></th><th>Disposable income<br>(1)</th><th>Wages<br>(2)</th><th>Unemployment Benefits<br>(3)</th><th>Social Security Benefits<br>(4)</th><th>Spending<br>(5)</th><th>Savings<br>(6)</th></tr></thead><tbody><tr><td colspan="7"><em>Variables</em></td></tr><tr><td>Months: -6</td><td>18.16<br>(12.81)</td><td>2.900<br>(9.992)</td><td>6.210**<br>(2.892)</td><td>14.41***<br>(3.902)</td><td>-21.50<br>(16.24)</td><td>-6.364<br>(22.97)</td></tr><tr><td>Months: -5</td><td>Ref</td><td>Ref</td><td>Ref</td><td>Ref</td><td>Ref</td><td>Ref</td></tr><tr><td>Months: -4</td><td>-37.68***<br>(13.38)</td><td>-29.88***<br>(9.651)</td><td>-4.194<br>(2.570)</td><td>3.295<br>(3.836)</td><td>-11.18<br>(15.94)</td><td>-64.88***<br>(23.64)</td></tr><tr><td>Months: -3</td><td>-44.26***<br>(14.23)</td><td>-41.45***<br>(10.10)</td><td>-5.984**<br>(2.922)</td><td>-2.980<br>(4.270)</td><td>-18.17<br>(16.66)</td><td>-30.54<br>(22.73)</td></tr><tr><td>Months: -2</td><td>32.61*<br>(17.27)</td><td>17.97<br>(13.83)</td><td>1.229<br>(3.441)</td><td>2.883<br>(4.819)</td><td>21.48<br>(18.05)</td><td>-12.58<br>(23.85)</td></tr><tr><td>Months: -1</td><td>453.8***<br>(26.85)</td><td>306.8***<br>(22.42)</td><td>37.87***<br>(4.992)</td><td>16.54***<br>(5.405)</td><td>96.28***<br>(19.67)</td><td>305.7***<br>(29.07)</td></tr><tr><td>Months: 0</td><td>-870.8***<br>(21.95)</td><td>-1,385.2***<br>(14.12)</td><td>299.4***<br>(11.79)</td><td>30.68***<br>(5.780)</td><td>-37.73*<br>(20.78)</td><td>-695.3***<br>(28.19)</td></tr><tr><td>Months: 1</td><td>-652.9***<br>(20.50)</td><td>-1,434.4***<br>(14.22)</td><td>593.1***<br>(11.71)</td><td>44.03***<br>(6.246)</td><td>-192.4***<br>(20.32)</td><td>-389.4***<br>(26.09)</td></tr><tr><td>Months: 2</td><td>-576.4***<br>(18.87)</td><td>-1,465.1***<br>(14.33)</td><td>775.0***<br>(9.926)</td><td>51.63***<br>(6.679)</td><td>-205.9***<br>(20.44)</td><td>-284.8***<br>(24.47)</td></tr><tr><td>Months: 3</td><td>-557.3***<br>(19.19)</td><td>-1,485.1***<br>(14.62)</td><td>831.8***<br>(9.281)</td><td>45.39***<br>(7.068)</td><td>-270.0***<br>(20.39)</td><td>-211.4***<br>(24.80)</td></tr><tr><td>Months: 4</td><td>-579.1***<br>(18.88)</td><td>-1,491.8***<br>(15.03)</td><td>846.7***<br>(9.131)</td><td>14.96**<br>(6.459)</td><td>-299.3***<br>(20.84)</td><td>-201.8***<br>(25.32)</td></tr><tr><td>Months: 5</td><td>-628.5***<br>(18.68)</td><td>-1,494.4***<br>(15.21)</td><td>842.4***<br>(9.431)</td><td>-14.89**<br>(6.525)</td><td>-287.4***<br>(22.05)</td><td>-238.5***<br>(25.70)</td></tr><tr><td colspan="7"><em>Fixed-effects</em></td></tr><tr><td>Household</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Calendar month</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td colspan="7"><em>Fit statistics</em></td></tr><tr><td>Observations</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td></tr><tr><td>R<sup>2</sup></td><td>0.71466</td><td>0.85851</td><td>0.72814</td><td>0.80374</td><td>0.67384</td><td>0.16882</td></tr><tr><td>Within R<sup>2</sup></td><td>0.14355</td><td>0.50214</td><td>0.45183</td><td>0.00805</td><td>0.01558</td><td>0.03675</td></tr></tbody></table>

Note. Event study estimates around the unemployment spell using the variables disposable income, savings and spending as outcomes. The reference period in the regression is 5 months before job loss. Clustered (ID) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1. Source. La Banque Postale sample.
<table><caption>Table E.2: Event study: decomposing the response to the unemployment shock, no control</caption><thead><tr><th></th><th>Disposable income<br>(1)</th><th>Wages<br>(2)</th><th>Unemployment Benefits<br>(3)</th><th>Social Security Benefits<br>(4)</th><th>Spending<br>(5)</th><th>Savings<br>(6)</th></tr></thead><tbody><tr><td colspan="7"><em>Variables</em></td></tr><tr><td>Months: -6</td><td>3.293<br>(12.67)</td><td>-10.26<br>(9.818)</td><td>8.547***<br>(2.822)</td><td>11.15***<br>(4.027)</td><td>-41.52**<br>(16.47)</td><td>-3.733<br>(22.84)</td></tr><tr><td>Months: -5</td><td>Ref</td><td>Ref</td><td>Ref</td><td>Ref</td><td>Ref</td><td>Ref</td></tr><tr><td>Months: -4</td><td>-27.99**<br>(13.42)</td><td>-17.28*<br>(9.679)</td><td>-3.143<br>(2.457)</td><td>-0.6970<br>(3.957)</td><td>12.90<br>(16.25)</td><td>-77.47***<br>(23.70)</td></tr><tr><td>Months: -3</td><td>-22.32<br>(14.14)</td><td>-19.48*<br>(10.08)</td><td>-3.251<br>(2.699)</td><td>-7.028<br>(4.388)</td><td>13.01<br>(16.83)</td><td>-39.86*<br>(22.48)</td></tr><tr><td>Months: -2</td><td>72.93***<br>(17.08)</td><td>52.88***<br>(13.66)</td><td>4.905<br>(3.165)</td><td>4.691<br>(4.928)</td><td>83.47***<br>(17.91)</td><td>-32.16<br>(23.47)</td></tr><tr><td>Months: -1</td><td>502.8***<br>(26.60)</td><td>349.8***<br>(22.12)</td><td>38.73***<br>(4.838)</td><td>24.16***<br>(5.479)</td><td>176.9***<br>(19.27)</td><td>282.5***<br>(28.58)</td></tr><tr><td>Months: 0</td><td>-822.1***<br>(21.31)</td><td>-1,336.5***<br>(13.74)</td><td>302.7***<br>(12.35)</td><td>35.47***<br>(5.751)</td><td>40.08**<br>(19.88)</td><td>-720.2***<br>(27.43)</td></tr><tr><td>Months: 1</td><td>-597.1***<br>(19.69)</td><td>-1,370.4***<br>(13.59)</td><td>595.9***<br>(12.07)</td><td>46.41***<br>(6.118)</td><td>-107.3***<br>(19.08)</td><td>-411.5***<br>(25.07)</td></tr><tr><td>Months: 2</td><td>-499.8***<br>(17.65)</td><td>-1,377.7***<br>(13.48)</td><td>777.0***<br>(9.994)</td><td>51.71***<br>(6.505)</td><td>-105.3***<br>(18.51)</td><td>-300.1***<br>(22.85)</td></tr><tr><td>Months: 3</td><td>-465.8***<br>(17.64)</td><td>-1,385.1***<br>(13.58)</td><td>833.4***<br>(9.330)</td><td>48.19***<br>(6.845)</td><td>-158.3***<br>(17.85)</td><td>-219.2***<br>(22.73)</td></tr><tr><td>Months: 4</td><td>-480.6***<br>(16.99)</td><td>-1,385.5***<br>(13.71)</td><td>849.3***<br>(9.192)</td><td>14.94**<br>(6.043)</td><td>-195.6***<br>(17.66)</td><td>-197.3***<br>(22.66)</td></tr><tr><td>Months: 5</td><td>-534.8***<br>(16.09)</td><td>-1,381.0***<br>(13.46)</td><td>842.6***<br>(9.564)</td><td>-20.73***<br>(6.018)</td><td>-185.4***<br>(18.22)</td><td>-238.4***<br>(22.51)</td></tr><tr><td colspan="7"><em>Fixed-effects</em></td></tr><tr><td>Household</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td colspan="7"><em>Fit statistics</em></td></tr><tr><td>Observations</td><td>28,908</td><td>28,908</td><td>28,908</td><td>28,908</td><td>28,908</td><td>28,908</td></tr><tr><td>R<sup>2</sup></td><td>0.64753</td><td>0.81873</td><td>0.65218</td><td>0.74871</td><td>0.65960</td><td>0.16782</td></tr><tr><td>Within R<sup>2</sup></td><td>0.21902</td><td>0.69084</td><td>0.58694</td><td>0.01333</td><td>0.03047</td><td>0.07543</td></tr></tbody></table>

Note. Event study estimates around the unemployment spell using the variables disposable income, savings and spending as outcomes. The reference period in the regression is 5 months before job loss. Clustered (ID) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1. Source. La Banque Postale sample.
<table><caption>Table E.3: Event study: decomposing the response to the unemployment shock (after 2021)</caption><thead><tr><th></th><th>Disposable income<br>(1)</th><th>Wages<br>(2)</th><th>Unemployment Benefits<br>(3)</th><th>Social Security Benefits<br>(4)</th><th>Spending<br>(5)</th><th>Savings<br>(6)</th></tr></thead><tbody><tr><td colspan="7"><em>Variables</em></td></tr><tr><td>Months: -6</td><td>24.12<br>(14.94)</td><td>15.32<br>(11.87)</td><td>9.641***<br>(3.342)</td><td>10.47**<br>(4.941)</td><td>-14.34<br>(19.47)</td><td>2.912<br>(28.23)</td></tr><tr><td>Months: -4</td><td>-40.87**<br>(15.97)</td><td>-31.61***<br>(11.30)</td><td>-4.568<br>(2.973)</td><td>-1.078<br>(4.936)</td><td>-4.738<br>(19.12)</td><td>-84.40***<br>(28.70)</td></tr><tr><td>Months: -3</td><td>-46.08***<br>(16.53)</td><td>-40.66***<br>(12.00)</td><td>-3.861<br>(3.298)</td><td>-5.177<br>(5.398)</td><td>-27.46<br>(19.73)</td><td>-30.73<br>(27.88)</td></tr><tr><td>Months: -2</td><td>29.36<br>(20.48)</td><td>20.73<br>(16.59)</td><td>4.554<br>(3.781)</td><td>-1.350<br>(5.827)</td><td>5.487<br>(21.53)</td><td>-2.053<br>(28.65)</td></tr><tr><td>Months: -1</td><td>438.1***<br>(31.86)</td><td>302.8***<br>(26.49)</td><td>38.23***<br>(6.120)</td><td>10.42<br>(6.699)</td><td>98.70***<br>(23.78)</td><td>278.7***<br>(35.32)</td></tr><tr><td>Months: 0</td><td>-890.6***<br>(25.62)</td><td>-1,419.3***<br>(16.25)</td><td>341.4***<br>(13.03)</td><td>29.32***<br>(7.183)</td><td>-40.98*<br>(24.91)</td><td>-699.0***<br>(34.56)</td></tr><tr><td>Months: 1</td><td>-666.5***<br>(24.34)</td><td>-1,469.5***<br>(16.21)</td><td>623.0***<br>(13.32)</td><td>43.75***<br>(7.786)</td><td>-193.1***<br>(24.47)</td><td>-389.9***<br>(31.73)</td></tr><tr><td>Months: 2</td><td>-590.6***<br>(22.55)</td><td>-1,494.5***<br>(16.89)</td><td>795.9***<br>(11.43)</td><td>51.32***<br>(8.332)</td><td>-209.3***<br>(24.56)</td><td>-305.5***<br>(29.64)</td></tr><tr><td>Months: 3</td><td>-574.6***<br>(23.15)</td><td>-1,521.9***<br>(17.46)</td><td>847.1***<br>(10.66)</td><td>46.63***<br>(8.812)</td><td>-264.0***<br>(24.53)</td><td>-210.6***<br>(30.31)</td></tr><tr><td>Months: 4</td><td>-623.6***<br>(22.18)</td><td>-1,540.2***<br>(17.84)</td><td>864.3***<br>(10.61)</td><td>9.422<br>(7.956)</td><td>-306.0***<br>(24.97)</td><td>-227.2***<br>(30.92)</td></tr><tr><td>Months: 5</td><td>-662.5***<br>(22.37)</td><td>-1,537.7***<br>(17.96)</td><td>857.8***<br>(11.07)</td><td>-17.26**<br>(8.216)</td><td>-313.8***<br>(26.00)</td><td>-230.5***<br>(31.08)</td></tr><tr><td colspan="7"><em>Fixed-effects</em></td></tr><tr><td>ID</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>month_integer</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td colspan="7"><em>Fit statistics</em></td></tr><tr><td>Observations</td><td>48,996</td><td>48,996</td><td>48,996</td><td>48,996</td><td>48,996</td><td>48,996</td></tr><tr><td>R<sup>2</sup></td><td>0.73307</td><td>0.86323</td><td>0.77720</td><td>0.81133</td><td>0.67805</td><td>0.17423</td></tr><tr><td>Within R<sup>2</sup></td><td>0.14899</td><td>0.48898</td><td>0.53810</td><td>0.01894</td><td>0.02108</td><td>0.03874</td></tr></tbody></table>

Note. Event study estimates around the unemployment spell using the variables disposable income, savings and spending as outcomes. The reference period in the regression is 5 months before job loss. Clustered (ID) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1. Source. La Banque Postale sample.
<table><caption>Table E.4: Event study: additional variables</caption><thead><tr><th>Dependent Variables:</th><th>Private<br>transfers<br>(1)</th><th>Energy<br>Bills<br>(2)</th><th>Direct debit<br>(without tax and credit)<br>(3)</th><th>Consumption<br>credit<br>(4)</th></tr></thead><tbody><tr><td><em>Variables</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Months: -6</td><td>-12.57***<br>(4.521)</td><td>0.6006<br>(0.9771)</td><td>-2.107<br>(3.959)</td><td>0.6706<br>(0.7713)</td></tr><tr><td>Months: -4</td><td>-7.245<br>(4.571)</td><td>0.1548<br>(0.9346)</td><td>1.499<br>(3.550)</td><td>0.8096<br>(0.6798)</td></tr><tr><td>Months: -3</td><td>-1.064<br>(4.746)</td><td>-0.6738<br>(0.8730)</td><td>-4.229<br>(3.911)</td><td>0.4239<br>(0.7029)</td></tr><tr><td>Months: -2</td><td>-10.48**<br>(4.878)</td><td>-0.6106<br>(1.063)</td><td>-0.6128<br>(4.298)</td><td>1.780**<br>(0.7377)</td></tr><tr><td>Months: -1</td><td>3.167<br>(5.354)</td><td>-0.0674<br>(1.034)</td><td>-2.074<br>(4.666)</td><td>1.317<br>(0.9131)</td></tr><tr><td>Months: 0</td><td>11.78**<br>(5.720)</td><td>-0.2786<br>(1.239)</td><td>-8.694*<br>(4.996)</td><td>1.249<br>(1.072)</td></tr><tr><td>Months: 1</td><td>8.653<br>(5.683)</td><td>-0.6829<br>(1.234)</td><td>-12.19**<br>(5.322)</td><td>2.624**<br>(1.081)</td></tr><tr><td>Months: 2</td><td>3.108<br>(5.949)</td><td>-1.680<br>(1.364)</td><td>-16.62***<br>(5.782)</td><td>2.513**<br>(1.259)</td></tr><tr><td>Months: 3</td><td>13.64**<br>(6.321)</td><td>-0.9511<br>(1.375)</td><td>-29.26***<br>(6.026)</td><td>1.716<br>(1.323)</td></tr><tr><td>Months: 4</td><td>12.07*<br>(6.269)</td><td>-2.433*<br>(1.476)</td><td>-43.34***<br>(6.338)</td><td>2.555*<br>(1.455)</td></tr><tr><td>Months: 5</td><td>15.38**<br>(6.702)</td><td>-1.223<br>(1.493)</td><td>-38.71***<br>(6.877)</td><td>2.600*<br>(1.573)</td></tr><tr><td><em>Fixed-effects</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Household<br>Calendar month</td><td>Yes<br>Yes</td><td>Yes<br>Yes</td><td>Yes<br>Yes</td><td>Yes<br>Yes</td></tr><tr><td><em>Fit statistics</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>57,816</td><td>57,816</td><td>57,816</td><td>57,816</td></tr><tr><td>R<sup>2</sup></td><td>0.48025</td><td>0.68453</td><td>0.85134</td><td>0.81907</td></tr><tr><td>Within R<sup>2</sup></td><td>0.00784</td><td>0.00648</td><td>0.01055</td><td>0.00639</td></tr></tbody></table>

Note. Event study estimates around the unemployment spell using the variables private transfers, energy bills, direct debit (without tax and credit), and consumption credit as outcomes in euros. The reference period in the regression is 5 months before job loss. Clustered (ID) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1. Source La Banque Postale sample.
# Liste des documents de travail récents de la Direction des Études et Synthèses Économiques*

2024/01 M. ANDRE, A. BOURGEOIS, E. COMBET, M. LEQUIEN, A. POTTIER
Challenges in measuring the distribution of carbon footprints : the role of product and price heterogeneity

2024/02 C. LE THI, M. SUAREZ CASTILLO, V. COSTEMALLE
Residential mobility and air pollution inequalities : describing income disparities in lifelong air pollution exposure

2024/04 P. AGHION, A. BERGEAUD, T. GIGOUT, M. LEQUIEN, M. MELITZ
Exporting Ideas : Knowledge flows from expanding trade in goods

2024/05 J. GIORGI, A. PLUNKET, F. STAROSTA DE WALDEMAR
Inter-regional highly skilled worker mobility and technological novelty

2024/06 M. HILLION
Une évaluation des achats transfrontaliers de tabac et des pertes fiscales associées en France

2024/07 A. BOURGEOIS, B. FAVETTO
Construction d'intervalle de confiance et relecture du passé avec le modèle Mésange

2024/08 M. ADAM, O. BONNET, E. FIZE, M. RAULT, T. LOISEL, L. WILNER
Cross-border shopping for fuel at the France-Germany border

2024/10 M. SUAREZ CASTILLO, D. BENATIA, C. LE THI, V. COSTEMALLE
Air pollution and children's health inequalities

2024/11 R. ABBAS, N. CARNOT, M. LEQUIEN, A. QUARTIER-LA-TENTE, S. ROUX
En chemin vers la neutralité carbone. Mais quel chemin ?

2024/12 M. LENZA, I. MOUTACHAKER, J. PAREDES
Density forecasts of inflation : a quantile regression forest approach

2024/16 A. QUARTIER-LA-TENTE
Utilisation des modèles de régression à coefficients variant dans le temps pour la prévision conjoncturelle

2024/18 P. GIVORD
How age at school entry affects future educational and socioemotional outcomes : Evidence from PISA

2024/19 D. BABET, M. CHABAUD
Follow the money ? Workers' mobility, wages and amenities

2024/20 A. MOUTAABBID
Les déterminants de la dynamique des salaires en France : approches macro et sectorielles par la courbe de Phillips

2024/21 C. GRISLAIN-LETREMY, J. SIXOU, A. SOTURA
Urban heat islands and inequalities : Evidence from French Cities

<footnote>* L'ensemble des documents est disponible sur le site Insee.fr et sur Repec.</footnote>