# Living through Lockdown: Social Inequalities and Transformations during the COVID-19 Crisis in France

Ettore Recchi, Emanuele Ferragina, Olivier Godechot, Emily Helmeid, Stefan Pauly, Mirna Safi, Nicolas Sauger, Jen Schradie, Katharina Tittel, Andrew Zola

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Ettore Recchi, Emanuele Ferragina, Olivier Godechot, Emily Helmeid, Stefan Pauly, et al.. Living through Lockdown: Social Inequalities and Transformations during the COVID-19 Crisis in France. 2020. [hal-03203721](https://HAL Id: hal-03203721)

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SciencesPo
OBSERVATOIRE SOCIOLOGIQUE
DU CHANGEMENT

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OSC
Papers

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n° 2020-1

July 2020

Living through Lockdown

Social Inequalities and Transformations during the COVID-19 Crisis in France

Ettore Recchi, Emanuele Ferragina,
Olivier Godechot, Emily Helmeid,
Stefan Pauly, Mirna Safi, Nicolas Sauger,
Jen Schradie, Katharina Tittel, Andrew Zola
# Summary

This working paper offers an overview of the first stage of the Coping with Covid (CoCo) project, which tracks the behaviors and attitudes of a representative panel of the French metropolitan population during the COVID-19 lockdown. We conducted five survey waves and administered daily journals of open-ended responses between April and June 2020 among a sample of 1,216 people from a pre-existing panel (ELIPSS). Earlier surveys of this sample allowed us to better contextualize changes that may have occurred during this unusual period.

We outline four experiential dimensions during the lockdown period: relation to work, everyday activities and time use, self-assessed health and well-being, and the framing of the pandemic crisis. What we found follows traditional inequality patterns and also reveals some unexpected changes in social practices and attitudes.

## Working (or not): Different Places, Different Fates (Chapter 2)

The transformation of work was unprecedented: in the first two weeks of the lockdown, only 58 percent of workers held on to their jobs, while the other 42 percent were either furloughed or put on leave. The share of working people increased progressively thereafter.

* Of those who continued working, half did so entirely from home and half stuck to their usual workplace.

* Upper and upper-middle class workers and above-median earners massively commuted to working from home, while lower and lower-middle classes, as long as they could continue to work, did so at the usual workplace.

* Working at one's usual workplace was correlated with exposure to a sensibly higher risk of infection by the virus over time.

* In contrast to working at the usual workplace, working from home shields against wage drops and COVID-19 infection. However, it closely intertwines domestic and professional work, which may be a source of tensions, notably for home-working women with young children in dual-earning couples.

## Staying Put: Home and Close to It (Chapter 3)

The organization of everyday life changed dramatically in the context of restricted freedom of movement.

* Two weeks into the lockdown, almost 60 percent of individuals in our sample said they had stepped out of their home no more than once a week, although this proportion slowly declined.

* Nearly half of all women in the panel reported that they were doing more housework than before the lockdown, compared to only 29 percent of men.

* Women spent dramatically more time than men supervising their children's schoolwork.

* People living with kids and in cramped spaces were significantly more likely to experience family tensions.

* People with higher income were much more likely to have more computers/dispositives and a stable Internet connection, which were vital for working, schooling, shopping, and socializing.

## Feeling: Health and Well-Being (Chapter 4)

The evolution of the pandemic across our panel showed changes in individuals' health conditions and well-being.

* The proportion of the population claiming to have contracted COVID-19 increased from 7 percent in early April to 9 percent in late May-early June.

* Apart from the direct impact of the virus on physical health, we found that the majority of individuals were
not psychologically dejected by the unprecedented situation but actually felt better than beforehand.

* However, the subjective well-being of women, the most financially vulnerable, people living alone, and the unemployed lagged significantly behind the average.

## Framing: A Health or Economic Crisis? (Chapter 5)

The framing of the COVID-19 crisis, as either health or economic in nature, varied across social groups and over time.

* At the beginning of the lockdown, health concerns were stronger but swung to economic concerns over time.

* Women and elderly people tended to always place a stronger emphasis on health while the unemployed and wealthier focused on the economic impact of the crisis.

* In an experiment with respondents, the malleability of opinions on the tradeoff between economic and health concerns reveals the uncertainty created by contradictory information and untested policy options.

We found two major changes related to the lockdown. On the one hand, everyday work practices and locations were either interrupted or transformed. As working online gained traction among the upper-middle class, it created a new divide with people from other social groups who either continued to commute to their usual (and riskier) workplaces or were suddenly furloughed. On the other hand, we recorded a subjective change in well-being that was surprisingly higher than before the pandemic for most people but lagged behind for the less privileged.

Overall, the crisis did not consign everyone to the same situation, as pre-existing inequalities persisted; in particular, women, the financially vulnerable, and the unemployed seemed to suffer the most on many levels, objectively and subjectively. Other groups who are by default under-represented in a general population survey like ours—immigrants, residents of the poorest neighborhoods, the homeless, people living in retirement homes, and those without Internet access—were also potentially more exposed to the multifaceted risks and costs entailed by the pandemic and the lockdown than average French residents.
Introduction

In the 4th century BC, Aristotle declared in *Politics* that humans
are by nature 'social animals' for both their capacity for coopera-
tion and sociability. Yet with the COVID-19 pandemic beginning
in late 2019, our status as cooperative and sociable beings was
put under strain as sweeping measures involving total avoid-
ance of social contact became the new norm all around the
world. Suddenly, Aristotle's two dimensions of the 'social' were
decoupled and played against each other: increased societal co-
operation was demanded to suppress face-to-face sociability to
the lowest possible degree. This reaction to the epidemiological
crisis was more sociopolitical than strictly medical, and gave rise
to an unprecedented social experiment. Entire national popula-
tions were put under lockdown orders previously unseen during
peacetime with schools closed, shops and restaurants shut-
tered, and travel made difficult or even illegal. Interpersonal in-
teraction was discouraged outside the household, and the new
norm of 'social distancing' that had been imposed politically ex
*abrupto* became the cardinal rule, affecting every facet of life,
including work, family, social relations, education, and leisure.

In France, strict containment measures<fnref n="1" /> lasted eight weeks from March 17 until May 11, 2020, and were followed by a progressive relaxation starting with permission to leave home without an authorization form, and then the reopening of non-essential shops, restaurants, and parks throughout the months of May and June. Although all schools besides upper secondary (lycées) were once again in session by June 22, university students and many workers who had the capacity to work from home were told not to expect in-person gatherings to resume for many months. And while internal European Union borders were once again crossable starting on June 15, and borders with some non-EU countries by the beginning of July, long-distance and international travel continued to be discouraged by the French government.

All of these ongoing amendments to normal daily operations
went hand in hand with enormous economic difficulties. In the
first quarter alone, France's GDP fell 5.8 percent, which repre-
sented the greatest quarterly decline since 1949 (Insee 2020a).
Meanwhile, in March and April, France recorded a 26 percent
increase in mortality compared to the same period in 2019, with
a peak of 124 percent in the hardest hit *département*, Seine-
Saint-Denis (Insee 2020b). These indicators all point to the clear
conclusion that France's population, along with nearly everyone
else in the world, has withstood unprecedented change and
strain since the start of the year.

In order to track how the French populace has dealt with this
strain, we surveyed a nationally representative sample of over
1,000 residents of metropolitan France on a biweekly basis
during the lockdown from early April to early June for a total
of five waves,<fnref n="2" /> with the intention of administering a sixth and
final wave in the Fall. We used these first five survey waves as a
tool for monitoring how different social groups (according to
gender, age, social class, employment status and type, house-
hold and housing type, and geographic location) have reacted
to lockdown measures.<fnref n="3" /> We observed both social practices and
attitudes, to a large extent taking stock of pre-existing indica-
tors that had been applied to the same respondents in previous
years as a part of the ELIPSS longitudinal study, initially launched
in 2012. This quasi-experimental design permitted us to assess
the specific changes that occurred due to the lockdown and its
aftermath. In addition, we complemented this survey data by
collecting regular online diary entries that our respondents had
written and submitted on a voluntary basis in reaction to a spe-
cific set of open-ended questions. This helped us to gain further
insights into the changes that individuals experienced over the
course of these months.

<footnote n="1">1. These measures required everyone to carry self-declared authorization stating the intent of any movement from their place of residence, with allowance only for travel between home and essential work, essential stores such as supermarkets and pharmacies, essential doctor appointments or the hospital, to another home to assist family in need, or for physical exercise for no more than one hour per day and within a maximum radius of one kilometer from home.</footnote>

<footnote n="2">2. Wave 1: April 1-8; wave 2: April 15-22; wave 3: April 29-May 6; wave 4: May 13-20; wave 5: May 27-June 4. In addition to the authors of this working paper, the design and administration of the surveys also involved Emmanuelle Duwez, Mathieu Olivier and Bernard Cormin-boeuf.</footnote>

<footnote n="3">3. This paper presents a general synthesis of our results from the first five CoCo survey waves. For more information on the analyses that we have already conducted, which have also helped to inform the structure of this paper, please refer to the four policy briefs that served as real-time snapshots of our findings (Recchi et al. 2020b; Ferragina et al. 2020; Safi et al. 2020; Sauger et al. 2020) and a research note detailing our results on reports of well-being (Recchi et al. 2020a).</footnote>
Because our focus is double—observing both social *practices* and *attitudes*—we dedicate our data analysis in this paper first to what people *did*, and second to what people *felt* and *thought* over the course of the lockdown and the subsequent reopening. Chapter 1 is devoted to a presentation of the data gathered from the five survey waves, relevant variables and their structure, and the methods we use to look at the specific themes and information gathered during each wave. Chapters 2 and 3 concentrate on people's actions, with the former ('Working (or not): Different Places, Different Fates') emphasizing our findings on the inequalities associated with working from home, being furloughed, and being an essential worker; the latter chapter ('Staying Put: At Home and Close to It') focuses on domestic activities, particularly as they relate to the gendered division of labor, childcare and homeschooling, and trips out of the house. Chapters 4 and 5 deal with people's attitudes. Chapter 4 ('Feeling: Health and Well-Being') focuses on health indicators, stress levels, and self-reported measures of well-being. Section 5 ('Framing: Health or Economic Crisis?') analyzes opinions about the crisis, focusing particularly on the important tradeoff between health and economic damage.

# Chapter 1. Data and Methods

## 1.1. Data collection and data structure

The first stage of the CoCo project consists of five survey waves and an optional set of 33 open-ended daily journal questions, which were conducted from early April to early June among a panel of respondents originally recruited in 2012 for participation in ELIPSS. This probability-based panel is maintained by the CDSP (Center for Socio-Political Data of Sciences Po), and currently relies on a sample of 1,404 French residents. The respondents were initially drawn from census data and took part in face-to-face interviews to establish their capacity to participate in the panel.

Panelists participate in about 10 surveys per year, with an average response rate of nearly 85 percent. This aligns with the response rate for the CoCo survey which saw 1,216 respondents (87 percent of panelists) participate in at least one wave (Table 1.1). Given the high frequency of this survey, there was some attrition from one wave to the next, although 732 panelists participated

in every wave. Since attrition is likely to be non-random, we re-weighted observations in order to account for this potential bias. Final weights in this paper have been computed to take into account design effects from the initial stage, bias due to acceptance rate in the enrollment phase, and post-stratification including sex, age, education, and region.

Each survey wave is made up of two sets of questions: 1. Those that appear in at least two of the survey waves; 2. Those that are unique to individual waves. The former group mostly includes questions revolving around subjective well-being, health, and work situations, while the latter group mostly revolves around individual survey wave themes.

In the first wave, we also asked panelists if they wished to answer open-ended questions at the end of the general set of survey questions. This lockdown journal allowed respondents to describe their daily living practices, feelings, and meaning-making on key topics. In total, 783 respondents participated in the journal at least once. Respondents who agreed to participate in these 33 short journal entries administered between April 10 and May 30 answered three questions per day; the number of participants ranged from a high of 499 participants on the third day into the journal to 262 on the last day. 42 panelists participated in all 33 open-ended surveys while 103 panelists participated just once. The questions evolved from April 10 to May 30, but two remained consistent over time: 'Tell us about a positive time that you had yesterday. How were you feeling?' and 'Tell us about an unpleasant or difficult time from yesterday. How were you feeling?'

Unweighted descriptive statistics for the socio-demographic characteristics of the 732 panelists present in all of the survey waves are summarized in Table 1.2. The panel has near gender parity, with 47 percent male respondents. The largest age group is represented by individuals over 60 years old (46 percent), followed by those aged 40 to 59 (43 percent), and finally those less than 40 years old (11 percent). 93 percent of the respondents were born in France. Educational attainment is classified according to the EU-LFS coding for educational attainment (ISCED-2011, from 2014), as described in Table 1.3. 18 percent of the panelists have completed secondary education or post-sec-
<table><caption>Table 1.1. Description of the five CoCo survey waves (April-June 2020)</caption><thead><tr><th>Survey Wave</th><th>Topic</th><th>Number of Respondents</th><th>Share of all panelists</th></tr></thead><tbody><tr><td>April 1-8</td><td>Health, well-being and social inequalities</td><td>1076</td><td>77%</td></tr><tr><td>April 15-22</td><td>Education and deconfinement</td><td>998</td><td>71%</td></tr><tr><td>April 29-May 6</td><td>Social contact, home &amp; work conditions</td><td>1023</td><td>73%</td></tr><tr><td>May 13-20</td><td>Social and political attitudes</td><td>940</td><td>67%</td></tr><tr><td>May 27-June 4</td><td>Life after lockdown</td><td>973</td><td>69%</td></tr><tr><td>At least one wave</td><td></td><td>1216</td><td>87%</td></tr><tr><td>All waves</td><td></td><td>732</td><td>52%</td></tr></tbody></table>

<table><caption>Table 1.2. Descriptive statistics for the CoCo-panel (N = 732), unweighted</caption><tbody><tr><td><strong>Gender</strong></td><td>(response rate: 97.7%)</td></tr><tr><td>Male</td><td>46.6%</td></tr><tr><td>Female</td><td>53.4%</td></tr><tr><td><strong>Age</strong></td><td>(response rate: 97.7%)</td></tr><tr><td>Below 40 years</td><td>11.0%</td></tr><tr><td>Between 40 and 59 years</td><td>42.5%</td></tr><tr><td>60 years and above</td><td>46.4%</td></tr><tr><td><strong>Place of birth</strong></td><td>(response rate: 95.4%)</td></tr><tr><td>Born in France</td><td>92.5%</td></tr><tr><td>Foreign born</td><td>7.4%</td></tr><tr><td><strong>Education</strong></td><td>(response rate: 97.8%)</td></tr><tr><td>Less than High School</td><td>35.0%</td></tr><tr><td>High School Grad</td><td>18.0%</td></tr><tr><td>Some College/College Grad</td><td>20.7%</td></tr><tr><td>Post-Graduate</td><td>26.3%</td></tr><tr><td><strong>Household Income</strong></td><td>(response rate: 94.4%)</td></tr><tr><td>Lowest (less than €2000)</td><td>24.6%</td></tr><tr><td>Lower middle (between €2000 and €2999)</td><td>27.3%</td></tr><tr><td>Upper middle (between €3000 and €3999)</td><td>20.4%</td></tr><tr><td>Highest (€4000 and more)</td><td>27.6%</td></tr><tr><td><strong>City type</strong></td><td>(response rate: 100%)</td></tr><tr><td>Rural area</td><td>25.9%</td></tr><tr><td>Urban area 2000-0.2m inhabitants</td><td>38.5%</td></tr><tr><td>Urban area 0.2m-2m inhabitants</td><td>21.8%</td></tr><tr><td>Paris</td><td>13.6%</td></tr><tr><td><strong>Work situation before lockdown</strong></td><td>(response rate: 99.9%)</td></tr><tr><td>Job</td><td>50.0%</td></tr><tr><td>Retired</td><td>39.9%</td></tr><tr><td>Inactive/school</td><td>6.8%</td></tr><tr><td>Unemployed</td><td>3.1%</td></tr><tr><td><strong>Political orientation</strong></td><td>(response rate: 100%)</td></tr><tr><td>Left</td><td>34.6%</td></tr><tr><td>Centre</td><td>21.9%</td></tr><tr><td>Right</td><td>43.5%</td></tr><tr><td><strong>Manager/Professional</strong></td><td>(response rate: 90.2%)</td></tr><tr><td>No</td><td>62.6%</td></tr><tr><td>Yes</td><td>37.4%</td></tr></tbody></table>

<table><tbody><tr><td><strong>Financially vulnerable</strong></td><td>(response rate: 100.0%)</td></tr><tr><td>No</td><td>92.3%</td></tr><tr><td>Yes</td><td>7.7%</td></tr><tr><td><strong>Flat size less than 25sqm per person</strong></td><td>(response rate: 99.4%)</td></tr><tr><td>No</td><td>86.7%</td></tr><tr><td>Yes</td><td>13.3%</td></tr><tr><td><strong>Living alone</strong></td><td>(response rate: 99.7%)</td></tr><tr><td>No</td><td>78.8%</td></tr><tr><td>Yes</td><td>21.2%</td></tr><tr><td><strong>Living with kids</strong></td><td>(response rate: 99.7%)</td></tr><tr><td>No</td><td>62.7%</td></tr><tr><td>Yes</td><td>37.3%</td></tr><tr><td><strong>Occupation</strong></td><td>(response rate: 99.3%)</td></tr><tr><td>Manager/Professional (including intellectual professions)</td><td>34.0%</td></tr><tr><td>Clerk</td><td>33.8%</td></tr><tr><td>Technician (including other 'intermediate' professions)</td><td>16.8%</td></tr><tr><td>Artisan, Shopkeeper (including small entrepreneurs)</td><td>7.1%</td></tr><tr><td>Blue collar</td><td>4.8%</td></tr><tr><td>Farmer</td><td>2.1%</td></tr><tr><td>None</td><td>1.4%</td></tr></tbody></table>

**Occupation:** Answer to the question: 'To which of the following activities does your professional activity best correspond? If you are not currently working in a profession, indicate the activity of the last profession you worked in.'

**Financially vulnerable:** Coded based on the response to the question in CoCo-survey wave Apr 1-8, 2020: 'Suppose you had to urgently face an expense of € 400 that you had not foreseen. How would you settle this expense based on your current financial situation? Several answers are possible, if you had to use several means to raise this sum.' Response is coded YES for 'I would not be able to pay this expense.'

**Household income:** Answer to the question: 'In February 2020, taking into account all types of income received (salaries, allowances, pensions, etc.), what was the monthly earnings for your household as a whole.'
ondary non-tertiary education and are labelled as 'High school' from here onwards. 21 percent have completed short-cycle tertiary education or bachelor's degrees ('Some College/College Grad'), and 26 percent have completed master's and doctoral degrees ('Graduate degree').

The largest group of respondents live in mid-sized urban areas with populations between 2,000 and 200,000 (39 percent), followed by rural areas (26 percent), and larger urban areas with 200,000 to 2,000,000 inhabitants (22 percent). 14 percent live in the Paris urban area (Île-de-France). When asked about their work situation before lockdown, half of respondents indicated that they were in active employment, 40 percent in early retirement, 7 percent in school, homemaking, or inactive for other reasons, and 3 percent in unemployment on March 15, 2020. 35 percent of respondents self-classify as leaning politically left, 22 percent identify at the center, and 43 percent self-classify as leaning politically right.

Asked about which type of occupation their current or last professional activity best corresponds to, a third of respondents classify their occupation as *cadres* or an intellectual profession (labelled here as 'Managers/Professionals') and another third as a Clerk. The remaining third of respondents self-classify as follows: 17 percent indicate an intermediate profession such as technician or foreman (labelled here as 'Technician'), 8 percent

Artisan, Entrepreneur, or Shopkeeper (hereafter referred to as 'Artisan, Shopkeeper'), 5 percent Blue-collar, and 2 percent Farmer, with 1 percent indicating that they had never performed any professional activity.

In terms of household income, approximately one quarter of respondents earn less than €2,000 per month, 27 percent earn between €2,000 and €2,999 per month, slightly more than 20 percent earn between €3,000 and €3,999 per month, and 28 percent report earnings of €4,000 per month or more. 8 percent of respondents said that they would not be able to pay an unexpected €400 bill at the beginning of the lockdown period, indicating their financial vulnerability.

With regards to their living situation, 21 percent of respondents indicated that they lived alone at the beginning of the lockdown period, and 37 percent were living with children in the household at this time. 13 percent of respondents reported spending the lockdown period in living quarters measuring less than 25 m² per person.

Finally, the ELIPSS panel contains limitations common to most general population surveys, and most noticeably statistically under-reports marginal groups. Because the panel does not include those who are homeless or in hospital, retirement homes, or prison, we must occasionally limit the conclusions we draw

<table><caption>Table 1.3. Description of the categories of educational attainment</caption><thead><tr><td>EU-LFS coding</td><td>Label</td><td>Description</td><td>Response items</td></tr></thead><tbody><tr><td>Below 303</td><td>Below high school</td><td>No secondary education and first cycle of secondary education</td><td>No scholarly education<br>No degree but schooling up to elementary or middle school<br>No degree but education beyond middle school<br>CEP (Certificate of Primary Education)<br>BEPC, Elementary Certificate, Middle school Certificate<br>CAP, Certificate of vocational ability (brevet de compagnon)<br>BEP</td></tr><tr><td>304, 400</td><td>High school</td><td>Secondary education, post-secondary non-tertiary education</td><td>Baccalauréat général, brevet supérieur<br>Technological or vocational baccalaureate, professional or technician's certificate, BEA, BEC, BEI, BEH<br>Certificate of ability in Law (capacité en droit)</td></tr><tr><td>500, 600</td><td>Some college/ College degree</td><td>Short-cycle tertiary education and bachelor's degrees</td><td>Postsecondary local certificates BTS, Diploma in Technological Studies (DUT), degree in the social or health professions, nursing<br>Undergraduate degree</td></tr><tr><td>700, 800</td><td>Graduate degree</td><td>Master's and doctoral degrees</td><td>Graduate or postgraduate degree (including medicine, pharmacy, dentistry), engineering degree<br>Doctorate</td></tr></tbody></table>
from our findings. In addition, participation in ELIPSS requires basic Internet access, potentially skewing the panel's profiles away from groups less likely to have such access. Hereafter we indicate any findings that we believe may be hindered by these limitations.

## 1.2. Quantitative and qualitative data analysis

For cross-sectional responses to the closed-ended survey questions, we estimate regression models primarily using socioeconomic characteristics as explanatory variables. A typical specification can be written as

$$y_i = \alpha + \beta \times X_i + \epsilon_i$$

where i indexes a respondent, $y_i$ is the outcome variable and $\alpha$ is the intercept. $X_i$ is a vector of explanatory variables that includes socioeconomic variables such as age, gender, disposable household income, and education. If $y_i$ is a continuous variable, we estimate the above using OLS. For binary outcomes, we use logit models to estimate the coefficients of interest. When the dependent variable in question is nominal and there are more than two categories, we use multinomial logistic regressions.

For questions that were asked in multiple survey waves, we exploit the panel nature of our data. In particular, we utilize pooled models with robust cluster variance estimators (vce) and introduce a time fixed effect. The vce cluster option is applied at the individual level such that the standard errors allow for intragroup correlation, relaxing the usual requirement that the observations be independent. That is to say, the observations are assumed to be independent across different respondents but not necessarily across the different survey waves for each respondent. Hence, unobservables are allowed to correlate on the individual level. This leads us to the following estimating equation

$$y_i = \alpha + \beta \times X_i + \gamma_t + \epsilon_i$$

where t indexes the survey wave and i again refers to the respondent. $\gamma_t$ is the time fixed effect (i.e. one dummy variable for each survey wave).

As mentioned above, we constructed survey weights that account for multiple sources of bias. When reporting descriptive statistics for outcome variables of interest, we always report weighted results in order to make our analysis representative of the population of France. For the different types of regression

analysis used in this working paper we do not weight observations. We assume that our control variables project out the endogeneity in our error term stemming from the different kinds of biases described above. If this is the case, our estimates are consistent and weighting could harm their precision (Solon, Haider & Wooldridge 2015). Our assumption here is consistent with the weights that we apply when reporting descriptive statistics in a sense that the variables used for constructing weights are a subset of the control variables used in regressions. To estimate relationships between ordinal dependent variables (such as 'Never,' 'Occasionally,' 'Always') and our independent variables, we utilize ordered logit models.

The method of analysis for the qualitative data (i.e. the open-ended survey responses) is an iterative process combining both inductive and deductive processes and is currently in a preliminary stage. The inductive coding is based on emergent themes, often called grounded theory (Strauss & Corbin 1994). The deductive coding is based on two approaches: one is derived from the literature's expectations and has a theoretical basis (Burawoy 1998), and the other is a series of robustness checks and cross-survey analysis based on the survey's quantitative findings. These two analytic approaches employ both manual and automatic coding. While word frequency and clustering, including a hierarchy of codes, is used as a first step in this coding process of topic modeling, this process is heavily supervised with the concurrent process of hand-coding for thematic meaning beyond practices or sentiments.

# Chapter 2. Working (or not): Different Places, Different Fates

## 2.1. Introduction

The lockdown is a unique and unprecedented social experience with considerable consequences for the labor market. How did it affect workers and work conditions in France? Did it attenuate or amplify labor-related inequality? Because the home-workplace separation is a mainstay of normal labor market operations in industrial and post-industrial societies, the universal prescription to stay home has meant a noticeable shift in work conditions and inequalities. While the past several decades have witnessed the increasing prevalence of technologies that permit remote
access to work and thus greater temporal and spatial flexibility in work tasks, these new services are unequally distributed across jobs (Felstead, Jewson, Phizacklea & Walters 2002). In fact, jobs that involve more independent tasks and autonomous decision-making—attributes of higher management and professional roles—are more easily performed from home, leading to higher reported levels of individual satisfaction (Golden & Veiga 2005). With the sudden and unprecedented prescription to stay home, the wide variations in requirements and routines from one job to the next have certainly exacerbated inequalities in work conditions. We will illustrate the effects of labor market shifts during the lockdown on social inequalities in the following section, and in Section 3 we will analyze the effect of work during the lockdown on health inequalities.

## 2.2. The restructuring of work situations under lockdown

The lockdown in France has had a very pronounced effect on employment conditions in spite of the fact that the government has tried to guarantee continuity with a generalized system of employer assistance (Ministère de l'Économie et des Finances 2020). In our survey waves, we notice two primary effects of this inevitable disruption on workers: the temporary discontinuation of everyday occupational activities for some, and the tricky question of work location for many others. In both regards, our results show a considerable shock at the beginning of lockdown, with a gradual recalibration thereafter. In the first wave of our survey, no more than 58 percent of workers were able to continue working regularly, while the other 42 percent were either furloughed or put on leave (parental, sickness or paid

holidays). Of those who continued working, half did so entirely from home. By late April into early May, the proportion of people working rose to 66 percent, with a slightly declining share of them working from home. Once strict lockdown measures had been lifted by the end of May and early June, 84 percent of pre-lockdown workers were back, indicating a trend towards a gradual return to work (Table 2.1).

In addition to regulatory measures such as short-time working and parental leave, home-based working rates skyrocketed during lockdown, constituting the most significant transformation in work patterns. Because the ELIPSS survey measured home-working rates in 2019, we were able to determine the extent of this transformation and the ways in which it affected different categories of workers. Before the start of lockdown, just over 4 percent of workers were mainly working from home; this proportion increased sharply between mid-March and mid-April 2020, when it reached 50 percent of the employed workforce and 29 percent of all pre-lockdown workers, only to decline to 21 percent around the beginning of June. The proportion of workers who worked exclusively away from home (reporting never working remotely) followed a symmetrical pattern, decreasing at the beginning of lockdown and increasing towards the end. The lockdown seems to have polarized jobs between those that are and those that are not 'home-workable' as reports of remote work on a 'regular,' 'occasional' or 'rare' declined in our survey waves. While one third of the workforce in 2019 reported working from home 'intermittently, occasionally, or rarely,' this figure fell to around 3 percent during lockdown.

<table><caption>Table 2.1. Work situation before, during, and after the lockdown</caption><thead><tr><th></th><th>Before lockdown</th><th>Apr 1-8, 2020</th><th>Apr 29-May 6, 2020</th><th>May 27-June 4, 2020</th></tr></thead><tbody><tr><td>Always/Mainly work remotely</td><td>4.3%</td><td>29.0%</td><td>29.1%</td><td>21.1%</td></tr><tr><td>Regularly/Alternating between workplace and remote work</td><td>10.2%</td><td>3.4%</td><td>3.3%</td><td>12.4%</td></tr><tr><td>Occasionally work remotely</td><td>8.4%</td><td></td><td></td><td></td></tr><tr><td>Rarely work remotely</td><td>13.8%</td><td></td><td></td><td></td></tr><tr><td>Never work remotely/ Work at (external) workplace</td><td>63.3%</td><td>25.7%</td><td>33.9%</td><td>50.0%</td></tr><tr><td>On leave (sick leave, parental leave, holidays)</td><td></td><td>16.2%</td><td>13.4%</td><td>4.4%</td></tr><tr><td>Unemployed (Partial or full unemployment)</td><td></td><td>25.7%</td><td>23.5%</td><td>12.1%</td></tr><tr><td>N</td><td>587</td><td>586</td><td>482</td><td>460</td></tr></tbody></table>

Note: Before lockdown: Answer to the question 'Before March 15, did you ever work remotely?' For all subsequent dates: Answers to the questions: 'And currently, you are...' and 'In the last two weeks, you have mainly worked from...' Calculated for those who were in active employment before March 15.
<table><caption>Table 2.2. Occupations and work situation on wave 3 (late April-early May) (N=482)</caption><thead><tr><td>Occupation</td><td>Work at usual workplace</td><td>Remote work (mainly or partial)</td><td>Unemployed (including partial unemployment)</td><td>Leave</td><td>Total</td><td>% by column</td></tr></thead><tbody><tr><td>Farmer</td><td>100.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>100.0%</td><td>1.4%</td></tr><tr><td>Blue collar</td><td>55.1%</td><td>3.3%</td><td>33.1%</td><td>8.5%</td><td>100.0%</td><td>10.7%</td></tr><tr><td>Clerk</td><td>40.4%</td><td>11.5%</td><td>31.0%</td><td>17.1%</td><td>100.0%</td><td>26.6%</td></tr><tr><td>Artisan &amp; Shopkeeper</td><td>31.2%</td><td>21.5%</td><td>42.4%</td><td>4.8%</td><td>100.0%</td><td>4.7%</td></tr><tr><td>Technician</td><td>25.8%</td><td>33.1%</td><td>22.8%</td><td>18.3%</td><td>100.0%</td><td>30.0%</td></tr><tr><td>Manager &amp; Professional</td><td>19.3%</td><td>49.1%</td><td>18.7%</td><td>12.9%</td><td>100.0%</td><td>26.6%</td></tr></tbody></table>

While it is likely that some jobs that are not 'home-workable' have switched to short-time working or leave—which may over-exaggerate the proportion of remote workers in active employment—the increase in regular home-work remained a tangible reality throughout the period, persisting even after the end of lockdown. However, this proportion remains below economists' estimates that 35-40 percent of jobs in the US and France are 'home-workable' (Dingel & Neiman 2020).

Occupation type is a primary determining factor of the differences in employment status and place of work (i.e. continuing in the usual location or moving into the home) during lockdown (Table 2.2). By the end of April, 85 percent of Farmers, Blue-collar workers, and Clerks who were able to continue working did so in their usual location. On the other hand, Managers/Professionals, along with Technicians, were significantly more likely to be working from home. For many occupations, nearly everyone had to stop working, which mainly meant being on leave for those in upper-level occupations and furloughed for those in lower-level occupations. These results are confirmed by other surveys (Lambert et al. 2020a; Dingel & Neiman 2020).

Given the association between occupations and average salaries, location of work was also related to wage inequalities. At the end of April, only 15 percent of the bottom-half of earners were able to work at home compared to 48 percent of middle to high earners. As a result, among those in the bottom half of the wage distribution, 41 percent continued commuting to their workplace during the lockdown, in contrast with 20 percent of middle to high earners, and 27 percent of top decile earners.

Under lockdown, traditional gender roles redoubled. By the end of April women, compared to men, were slightly less likely to

be working at home (25 vs. 33 percent) and more likely to be unemployed (28 vs. 22 percent). Only 13 percent of women who had a child under the age of six worked outside the home, compared to 36 percent of men in this situation. The proportion of women with young children who were unemployed or on leave approached twice that of men (69 vs. 42 percent). This finding resonates with further analyses showing that childcare, in the absence of ordinary schooling and childcare facilities, fell mostly on women's shoulders (see Chapter 3). Moreover, once we control for socio-demographic variables, notably occupation (Table 2.3), we find that women worked significantly more from home than at their usual work location.

Table 2.3 digs deeper into the determinants of remote working during the lockdown. Beforehand, remote work was mostly determined by occupation, and to some extent by living in Paris and its surrounding region (Île-de-France) (analysis not shown). Self-employed Artisans/Shopkeepers and Managers/Professionals (e.g. journalists, researchers, some doctors, etc.) were already likely to work from home. With the beginning of lockdown however, a significant proportion of workers not used to home-working made the switch. As a consequence, the determinants of working from home changed, detaching this form of work from a limited set of occupations. Women, those holding a graduate degree, and earners above the income median became most likely to work remotely.
<table><caption>Table 2.3. Logit regressions of work situation during and before lockdown on selected socio-demographic characteristics</caption><thead><tr><th rowspan="2"></th><th colspan="3">1. Multinomial logit (Ref=Working outside home)</th><th rowspan="2">2. Ordered Logit: Frequency of teletravail before lockdown (where 1=Never and 5=Always)</th><th rowspan="2">3. Logit: Doing alternating or mainly tele-travail during lockdown</th></tr><tr><th>On leave (sick or parental leave or holidays)</th><th>Unemployed (Partial or full unemployment)</th><th>Remote work (mainly or partial)</th></tr></thead><tbody><tr><td>Age 35-45 years (Ref=below 35)</td><td>0.190<br>(0.563)</td><td>0.953<br>(0.656)</td><td>0.244<br>(0.476)</td><td>-0.138<br>(0.516)</td><td>0.024<br>(0.402)</td></tr><tr><td>Age above 49 years (Ref=below 35)</td><td>-0.291<br>(0.301)</td><td>0.267<br>(0.336)</td><td>-0.174<br>(0.262)</td><td>-0.099<br>(0.260)</td><td>-0.132<br>(0.232)</td></tr><tr><td>Woman</td><td>-0.371<br>(0.285)</td><td>0.070<br>(0.338)</td><td>0.439*<br>(0.267)</td><td>-0.286<br>(0.242)</td><td>0.556**<br>(0.232)</td></tr><tr><td>Foreign born</td><td>0.053<br>(0.419)</td><td>-0.322<br>(0.653)</td><td>0.268<br>(0.446)</td><td>0.042<br>(0.449)</td><td>0.310<br>(0.391)</td></tr><tr><td>Living alone</td><td>-0.230<br>(0.374)</td><td>0.657<br>(0.466)</td><td>0.248<br>(0.363)</td><td>0.431<br>(0.343)</td><td>0.227<br>(0.326)</td></tr><tr><td>Living with kids</td><td>-0.059<br>(0.298)</td><td>0.713*<br>(0.394)</td><td>0.200<br>(0.287)</td><td>0.130<br>(0.272)</td><td>0.110<br>(0.259)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.290<br>(0.397)</td><td>0.053<br>(0.45)</td><td>0.559<br>(0.412)</td><td>0.383<br>(0.448)</td><td>0.441<br>(0.388)</td></tr><tr><td>Some college (Ref=below High school)</td><td>0.055<br>(0.369)</td><td>-0.039<br>(0.432)</td><td>0.222<br>(0.390)</td><td>0.751*<br>(0.412)</td><td>0.188<br>(0.357)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.010<br>(0.456)</td><td>-0.162<br>(0.488)</td><td>0.714*<br>(0.421)</td><td>0.701<br>(0.445)</td><td>0.725*<br>(0.374)</td></tr><tr><td>Wage median - P90% (Ref=below median)</td><td>-0.093<br>(0.320)</td><td>0.786**<br>(0.327)</td><td>0.904***<br>(0.289)</td><td>-0.029<br>(0.296)</td><td>0.799***<br>(0.257)</td></tr><tr><td>Wage top 10% (Ref=below median)</td><td>-1.185*<br>(0.629)</td><td>0.832<br>(0.629)</td><td>0.680<br>(0.456)</td><td>0.292<br>(0.42)</td><td>0.896**<br>(0.380)</td></tr><tr><td>Financially vulnerable</td><td>0.738**<br>(0.369)</td><td>-0.377<br>(0.489)</td><td>0.389<br>(0.515)</td><td>-0.111<br>(0.458)</td><td>0.170<br>(0.473)</td></tr><tr><td>Artisan, Shopkeeper (Ref=Blue collar and Farmer)</td><td>0.378<br>(0.584)</td><td>0.760<br>(0.744)</td><td>1.221<br>(0.845)</td><td>1.809**<br>(0.735)</td><td>1.063<br>(0.806)</td></tr><tr><td>Clerk (Ref=Blue collar and Farmer)</td><td>0.488<br>(0.412)</td><td>1.171*<br>(0.602)</td><td>1.354**<br>(0.669)</td><td>-0.067<br>(0.630)</td><td>1.067<br>(0.665)</td></tr><tr><td>Manager/Professional (Ref=Blue collar and Farmer)</td><td>0.972*<br>(0.563)</td><td>0.721<br>(0.687)</td><td>2.605***<br>(0.706)</td><td>1.739***<br>(0.668)</td><td>2.255***<br>(0.684)</td></tr><tr><td>Technician (Ref=Blue collar and Farmer)</td><td>0.607<br>(0.518)</td><td>0.846<br>(0.700)</td><td>2.000***<br>(0.706)</td><td>0.758<br>(0.654)</td><td>1.754**<br>(0.692)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.389<br>(0.474)</td><td>-0.041<br>(0.448)</td><td>0.145<br>(0.397)</td><td>-0.458<br>(0.350)</td><td>0.309<br>(0.323)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>-0.520<br>(0.451)</td><td>0.236<br>(0.444)</td><td>-0.034<br>(0.391)</td><td>-0.645*<br>(0.357)</td><td>0.118<br>(0.325)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.977**<br>(0.452)</td><td>-0.272<br>(0.449)</td><td>-0.572<br>(0.423)</td><td>-0.744**<br>(0.374)</td><td>-0.207<br>(0.363)</td></tr><tr><td>Flat size less than 25sqm p.p.</td><td>0.323<br>(0.328)</td><td>0.299<br>(0.354)</td><td>0.182<br>(0.343)</td><td>0.040<br>(0.328)</td><td>0.027<br>(0.31)</td></tr><tr><td>Coco3 (Apr 29-May 6 2020)</td><td>-0.379**<br>(0.148)</td><td>-0.385*<br>(0.208)</td><td>-0.317**<br>(0.141)</td><td></td><td>-0.090<br>(0.110)</td></tr><tr><td>Coco5 (May 27-June 4 2020)</td><td>-1.726***<br>(0.204)</td><td>-1.579***<br>(0.269)</td><td>-0.902***<br>(0.153)</td><td></td><td>-0.156<br>(0.132)</td></tr><tr><td>/cut1</td><td></td><td></td><td></td><td>1.371*<br>(0.711)</td><td></td></tr><tr><td>/cut2</td><td></td><td></td><td></td><td>1.926***<br>(0.715)</td><td></td></tr><tr><td>/cut3</td><td></td><td></td><td></td><td>2.752***<br>(0.721)</td><td></td></tr><tr><td>/cut4</td><td></td><td></td><td></td><td>4.258***<br>(0.747)</td><td></td></tr><tr><td>Constant</td><td>0.335<br>(0.608)</td><td>-2.556***<br>(0.736)</td><td>-2.543***<br>(0.793)</td><td></td><td>-3.420***<br>(0.759)</td></tr><tr><td>Observations</td><td></td><td>1,076</td><td></td><td>384</td><td>1,076</td></tr></tbody></table>

Note: Robust cluster standard errors by individuals in parentheses (except in model 2 where we use classical standard errors). Waves 1, 3, and 5 are pooled in model 1 and 3. The model on pre-lockdown remote work is based on wave 1. Logit, ordered logit and multinomial logit display comparable parameters. *** p<0.01, ** p<0.05, * p<0.1
We studied the implications of these changes in work conditions on a wide range of work-related variables. First, working from home provided more favorable conditions. Despite the fact that professional conflicts were rare during this period, working away from home does seem to have triggered some tensions. This finding is consistent with workers' preferences for future work location: lockdown home-workers demonstrate a much stronger desire than others to continue avoiding working exclusively at their usual place of work (22 percent of home-workers compared to 62 percent of other workers, 65 percent of workers on leave, and 50 percent of furloughed workers).

Second, our data also allow us to measure the ways in which the transformation of work conditions affected wages. The result is indisputable: 21 percent of those who kept commuting to their workplace reported a decrease in their wages compared to only 2 percent of home-workers. Although initially conditioned on occupational and wage inequalities, home-working appears to have accentuated these inequalities during lockdown.

As a consequence of the massive spread of work-from-home practices, the home suddenly became the site of both paid and unpaid labor (see Chapter 3). This overlap is all the more visible and complex to manage for dual-earning couples, given that it contradicts traditional domestic gender roles, bringing both innovations and tensions. Our index of the share of housework performed by men moves from 33 to 41 percent when the woman works at home rather than at her usual place of work. It also moves by a similar magnitude when the man is out of work (on leave or furloughed) instead of working at his usual place of work. This increased involvement of male partners in domestic work nevertheless hits a limit when it comes to caring for young children. While tensions in couples are not correlated with men performing housework (cooking, cleaning, shopping), they do increase substantially with the presence of young children, especially when the woman works from home and the man is out of work. In fact, on a 100-point scale, couples with these characteristics experience 18 to 22 percent more reports of tension than households with older children or where the man works at his usual place of work and the woman does not work. This might suggest that men have difficulty accepting the need to pull more weight when it comes to childrearing and, symmet-

rically, that women with young children have difficulty getting their partners to accept that they can be workers and not just mothers at home.

Although remote work seems to have been a rather privileged situation during lockdown compared to continuing to work outside the home—which often heightens the fear of infection (expressed in some diaries: ‘Afraid of this invisible virus in my workplace. Feeling powerless…’, ‘suspected case at my place of work. Worried about everyone being in danger’)<fnref n="4" />—home-working also comes with hardships and tensions. In their diaries about good and bad moments of life in lockdown, our panelists voiced diverging views about remote work. On the negative side, some associate it with ergonomic difficulties:

> *Retinal migraine for the third day in a row, except this time instead of coming on after work, it hit me around 4pm making it impossible to work since they always start out by blurring and shifting my vision. So basically impossible to focus on such a small screen. After half an hour, my vision went back to normal, but then it was the terrible headache that took over. I was able to work a little but forget being able to concentrate. In short, long live 8 hours per day of home-working on a tiny screen. I'm afraid it's becoming the new normal.<fnref n="5" />*

Respondents also often blamed poor Internet connection for their home-working difficulties. Others, however, praised the fact that remote work enabled them to have more time and a greater capacity to combine work with a close proximity to nature. For instance, when thinking of a good moment during the day, one respondent wrote that ‘lockdown has allowed us to work more from the garden and in a more relaxed state, seeing that we have some time ahead of us,’ while two others state that ‘I worked in the garden yesterday under the sun and among the

<footnote n="4">4. ‘Peur de ce virus invisible dans mon environnement de travail. Sentiment d’impuissance…,’ ‘suspicion de cas sur mon lieu de travail. Inquiétude quant à la mise en danger de tous.’</footnote>

<footnote n="5">5. ‘Migraine ophtalmique pour le troisième jour consécutif, sauf qu’au lieu d’être après ma journée de travail, elle m’est tombée dessus vers 16h, impossible de travailler car ça commence toujours par une vision trouble et comme décalée. Alors impossible de fixer un si petit écran. Après une demi-heure, ma vision est redevenue normale, mais c’est le mal de tête intense qui a pris le relais. J’ai pu travailler un peu mais bonjour la concentration. Bref vive le télétravail 8h par jour sur écran minuscule. J’ai peur que cela devienne constant.’</footnote>
violets and buttercups. Fantastic' and 'Watched a squirrel from my window while I worked.'<fnref n="6" />

The conditions of cheerfulness nevertheless rest on finding the correct division of domestic labor:

> I managed to get nearly a whole day of work done at home since my partner was able to free himself up to take care of the kids. That was the first time in 15 days.

> Having to manage working from home and the educational continuity of my kids, I have a feeling of being oppressed or suffocating, along with the dissatisfaction of not being able to please everyone.<fnref n="7" />

## 2.3. Work and health risks under lockdown

The goal of both lockdown and the government's plea for remote work was to cut the rate of infection. However, many workers—especially those in the working-class and lower middle-class—only had the option of continuing to work from the usual location. Did this unequal reshuffling of work situations increase health inequalities? Indeed, our survey shows that employment conditions have had a marked impact on the probability of reported infection, with respondents who kept commuting to their place of work more likely to contract the virus over the period we surveyed.

While the spread of the virus was mostly driven by regional variations throughout early April, our data from early May and after demonstrate a correlation between COVID-19 infection rates and work situations. Of those working outside the home, 13.3 percent say that they have been infected vs. only 6.2 percent of remote workers. The first column of Table 2.4 confirms the fact that continuing to commute remains significantly correlated

with suspicions of COVID-19 infection, even when we control for a large set of covariates.

That said, the causal relation between infections and work situation could be biased. Some respondents may have been infected in March and once cured, could have gone back to work, while those who were still infected could have opted for sick leave or remote work. To more specifically assess the role of work situations in the spread of COVID-19, we restrict our sample in Table 2.4 (Columns 2 to 4) to respondents who had not (yet) contracted the virus in the first wave of the survey. This design enables us to better characterize the specific contribution of work situations in early April to the risk of being infected.

After controlling for typical socio-demographic covariates, we find that workers outside the home were three times more likely to declare becoming infected with COVID-19 at some point before the beginning of May. This result holds even after looking more closely at the specific COVID-19 symptoms they declare having experienced. The effect of continuing to commute is most pronounced for Clerks and Managers/Professionals. This is probably because in-person contacts are more frequent in these two occupations, either with the general public in the case of Clerks (like cashiers or caregivers) or with their team (such as in meetings) in the case of Managers/Professionals.

## 2.4. Conclusion

Early studies about the lockdown in France and elsewhere have already described those working in blue-collar occupations as less likely to be able to work from home (Lambert et al. 2020a; Dingel & Neiman 2020), suggesting that the large-scale transition of paid work into the home is easier to endure for white-collar workers and those in the upper segments of the income distribution. This socioeconomic divide is not surprising, especially given the spatial and temporal autonomy of managers and others in advantaged labor market positions (Felstead et al. 2002). Indeed, many of these higher positions are marked by greater self-discretion and independence in the completion of day-to-day activities, making home-working simpler to implement and less likely to negatively affect working conditions and job satisfaction (Golden & Veiga 2005). The shift in working conditions at home and in the workplace has also been exacerbated.

<footnote n="6">6. 'Le confinement nous permet de travailler davantage dans notre jardin dans une plus grande décontraction car nous avons du temps devant nous;' 'J'ai travaillé hier dans le jardin au soleil devant des vitrées et des boutons d'or. Génial;' 'Observer un écureuil depuis ma fenêtre pendant que je travaillais.'</footnote>

<footnote n="7">7. 'J'ai réussi à dérouler une journée de travail en télétravail quasi-entière, mon conjoint ayant pu se libérer pour garder les enfants. C'était la première fois en 15 jours ;' 'Devoir gérer en même temps mon télétravail et la continuité pédagogique de mes enfants, j'ai eu un sentiment d'oppression, d'étouffement et aussi d'insatisfaction de ne pouvoir satisfaire chacun.'</footnote>
<table><caption>Table 2.4. Logit and OLS regressions of suspecting Covid-19 infection between wave 1 (late March-early April) and wave 3 (late April-early May) on selected socio-demographic characteristics</caption><thead><tr><th></th><th>All March- April infections</th><th>New infections</th><th>New infections (interaction model)</th><th>Symptom index</th></tr></thead><tbody><tr><td>Working at work on April 1st-8th</td><td>0.671*<br>(0.354)</td><td>1.340**<br>(0.568)</td><td></td><td>0.250*<br>(0.138)</td></tr><tr><td>Age 40-60 (Ref=below 40 years)</td><td>-0.423<br>(0.356)</td><td>-0.000<br>(0.696)</td><td>-0.031<br>(0.699)</td><td>-0.328**<br>(0.138)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>-0.533<br>(0.404)</td><td>-0.800<br>(0.807)</td><td>-0.834<br>(0.811)</td><td>-0.562***<br>(0.151)</td></tr><tr><td>Woman</td><td>-0.309<br>(0.264)</td><td>-0.722<br>(0.486)</td><td>-0.829*<br>(0.502)</td><td>0.058<br>(0.088)</td></tr><tr><td>Foreign born</td><td>0.428<br>(0.399)</td><td>0.907<br>(0.627)</td><td>0.869<br>(0.640)</td><td>0.153<br>(0.158)</td></tr><tr><td>Living alone</td><td>0.338<br>(0.363)</td><td>0.297<br>(0.672)</td><td>0.285<br>(0.692)</td><td>0.306**<br>(0.128)</td></tr><tr><td>Living with kids</td><td>0.110<br>(0.305)</td><td>-0.859<br>(0.555)</td><td>-0.889<br>(0.571)</td><td>-0.003<br>(0.105)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.565<br>(0.528)</td><td>-0.924<br>(0.892)</td><td>-0.718<br>(0.880)</td><td>0.047<br>(0.148)</td></tr><tr><td>Some college (Ref=below High school)</td><td>0.678<br>(0.502)</td><td>-0.378<br>(0.762)</td><td>-0.202<br>(0.754)</td><td>0.006<br>(0.140)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.553<br>(0.546)</td><td>-0.237<br>(0.820)</td><td>-0.315<br>(0.817)</td><td>0.101<br>(0.160)</td></tr><tr><td>Financially vulnerable</td><td>0.355<br>(0.418)</td><td>1.283*<br>(0.704)</td><td>1.323*<br>(0.714)</td><td>0.250<br>(0.163)</td></tr><tr><td>Less than 25 sqm per person</td><td>0.221<br>(0.354)</td><td>0.247<br>(0.655)</td><td>0.379<br>(0.679)</td><td>0.153<br>(0.133)</td></tr><tr><td>Artisan/Shopkeeper (ref=Blue collar/Farmer)</td><td>0.133<br>(0.547)</td><td>-0.206<br>(1.144)</td><td>0.161<br>(1.205)</td><td>-0.084<br>(0.183)</td></tr><tr><td>Clerk/Technician (ref=Blue collar/Farmer)</td><td>0.331<br>(0.330)</td><td>0.710<br>(0.634)</td><td>1.057<br>(0.733)</td><td>0.084<br>(0.112)</td></tr><tr><td>Manager/Professional (ref=Blue collar/Farmer)</td><td>0.020<br>(0.436)</td><td>0.389<br>(0.797)</td><td>0.555<br>(0.897)</td><td>0.085<br>(0.141)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>-0.920**<br>(0.405)</td><td>0.411<br>(0.755)</td><td>0.522<br>(0.773)</td><td>-0.109<br>(0.128)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>-0.304<br>(0.408)</td><td>0.367<br>(0.860)</td><td>0.434<br>(0.867)</td><td>-0.168<br>(0.146)</td></tr><tr><td>Household income €4000 and more (Ref=below €2000)</td><td>-0.369<br>(0.419)</td><td>0.785<br>(0.888)</td><td>0.839<br>(0.929)</td><td>-0.017<br>(0.153)</td></tr><tr><td>Paris region (Ref=rural)</td><td>0.782*<br>(0.428)</td><td>0.445<br>(0.841)</td><td>0.391<br>(0.849)</td><td>0.045<br>(0.161)</td></tr><tr><td>Grand Est region</td><td>0.711**<br>(0.361)</td><td>0.231<br>(0.684)</td><td>0.177<br>(0.695)</td><td>0.173<br>(0.140)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=rural)</td><td>-0.195<br>(0.633)</td><td>0.991<br>(1.019)</td><td>1.050<br>(1.015)</td><td>0.048<br>(0.205)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=rural)</td><td>-0.357<br>(0.362)</td><td>-0.054<br>(0.704)</td><td>-0.043<br>(0.705)</td><td>-0.017<br>(0.126)</td></tr><tr><td>Working at work on April 1st * Other occupation</td><td></td><td></td><td>-0.018<br>(1.108)</td><td></td></tr><tr><td>Working at work on April 1st * Manager/Professional</td><td></td><td></td><td>1.993**<br>(0.960)</td><td></td></tr><tr><td>Working at work on April 1st * Clerk</td><td></td><td></td><td>2.581***<br>(0.952)</td><td></td></tr><tr><td>Constant</td><td>-2.447***<br>(0.723)</td><td>-3.842***<br>(1.295)</td><td>-4.130***<br>(1.397)</td><td>-3.248***<br>(0.240)</td></tr><tr><td>Observations</td><td>872</td><td>805</td><td>805</td><td>805</td></tr></tbody></table>

Note: Logistic regressions for columns 1 to 3, OLS in column 4. Standard error in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1

To capture new infections related to work situations, in Columns 2, 3, and 4 we exclude panelists who suspected being infected by COVID-19 in early April. Thanks to a logistic regression, we construct an index of COVID-19 symptoms as a prediction score of COVID-19 infection based on the following list of nine symptoms: cough, headache, tiredness, cold, sore throat, breathing difficulties, soreness, fever, stomach ache. We use this variable as a dependent linear variable in Column 4 for an OLS regression.
bated by the pandemic's health effects as workplace workers are overall more likely to report infection.

Gender inequality in work conditions during the lockdown was evident as well, not only because of increases in domestic responsibilities such as homeschooling tasks being divided unequally, but also because of the lockdown's disproportionate impact to highly gendered sectors such as hospitality or teaching (Alon, Doepke, Olmstead-Rumsey & Tertilt 2020). Although there are indications that both men and women are able to accommodate more family needs into their routines when working from home, women in this situation are faced with a more pressing expectation to prioritize domestic responsibilities over their role as a paid worker (Osnowitz 2005). As we have shown in this chapter, lockdown revealed and occasionally exacerbated the difficulties of balancing paid work with domestic responsibilities for women—and particularly mothers in dual-earner couples—as daily life and work retreated to the home.

## Chapter 3. Staying Put: At Home and Close to It

### 3.1. Introduction

As lockdown began in France and nearly everyone's movement was limited, domestic spaces became the epicenter of daily life. While the entire French population was ordered to stay home, inequalities both persisted and shifted, particularly across gender and class lines. In this chapter we delve into the organization of everyday life under the lockdown and how these changing living conditions were founded upon pre-existing inequalities. In Section 2, we focus on the frequency and length of outings; Section 3 looks more closely at individuals' time use; Section 4 explores relations with children and family tensions; Section 5 deals with digital inequalities at home; in the conclusion we pull the threads of the analyses together in terms of continuing and newly emerging inequalities.

### 3.2. 'Restez chez vous !':<fnref n="8" /> Coping with a new social norm

The lockdown turned everyone's ability to leave home from a routine into an exceptional privilege. Going out without an explicit reason was banned, and even beyond the legal sanctioning, a moral prescription—reiterated in regular speeches by

the President—likened staying in to civic responsibility. Did the French comply with these restrictions to personal freedoms we consider untouchable in 'normal times?'

According to their responses to our surveys, they did—with the caveat that the rate of compliance to these strict and unusual rules progressively declined over the course of lockdown.<fnref n="9" /> Two weeks into the lockdown period, almost 60 percent of our respondents said they had gone out no more than once a week. This proportion declined to 52 percent two weeks later, and to 45 percent one month later. Not surprisingly, working people were significantly more likely to go out on a daily basis. In fact, a strong predictor of a (almost) daily outing is age: people over 40 were significantly over-represented among those more likely to go out. Although with less and intermittent statistical significance, women and Parisians stayed at home more. For women, going out less might be an effect from an overloaded domestic burden, especially in the first weeks of lockdown, and their higher concern for health (see Chapter 5); for residents of smaller towns, going out more could be a result of easier access to green areas or looser social sanctioning in less densely populated areas. Police patrolling was also a potentially significant deterrent in Paris. Overall, 17 percent of our sample reported being stopped by police officers during lockdown with little heterogeneity (though there was a higher chance of being stopped for people in the lower-middle range of household income and with the lowest level of education). Encounters with the police were, however, not particularly conflictual with less than five percent of respondents reporting a negative attitude towards these stops. Regardless, some strong opinions did surface: 'Control by the gendarmes [national guards] again and again, leave those who are working alone, feeling of childishness for the controls.'<fnref n="10" />

<footnote n="8">8. 'Stay home!'</footnote>

<footnote n="9">9. Analyses of individual mobility using cell phone data show that indeed movements between French departments were suddenly cut by 60 percent at the beginning of the lockdown, only to recover progressively thereafter and be back to pre-lockdown levels by the third week of June (Santamaria et al. 2020). Interestingly, the decrease in mobility in France was almost as dramatic as in Spain, which had the steepest drop in Europe (Ibid).</footnote>

<footnote n="10">10. 'Contrôler [sic] par les gendarmes encore et encore, laisser [sic] tranquille ceux qui bosse, sentiment enfantillages pour les contrôles.'</footnote>
Our survey measured two other indicators of compliance with the lockdown rules, including the frequency of meetings with relatives and friends, and the length of time spent outside. Two weeks into the lockdown, 90 percent of respondents had not met their friends and 83 percent had not met their relatives within the previous fortnight. These proportions declined to 86 and 77 percent respectively one month into the lockdown, and to 80 and 72 percent 45 days into the lockdown.<fnref n="11" /> We also asked our respondents if they had gone out for more than one hour per day, something not recommended, although still legally feasible under certain conditions. Overall, in our last survey before the end of strict lockdown measures, 40 percent stated that they had done it 'sometimes' (21 percent) or 'often' (19 percent). Only 22 percent of the sample reported that they had never left their home for more than one hour. The categories of respondents who were significantly more likely to spend more than sixty minutes away from home were workers (not surprisingly, given the occasional necessity to continue leaving home to work), people aged 40 to 60, and people living in small towns and rural areas. On the contrary, women and those we consider financially vulnerable (people incapable of paying an unexpected €400 bill) were less likely to declare going out for extended periods of time. To some extent, compliance with lockdown seems to reflect the degree of social control that accompanied this new social norm, and also inequalities (for women and the poor), although not for the elderly who, in spite of the higher risk associated with the virus, did not report a higher rate of staying at home.

**3.3. Time-use: Gender differences and evolution over the lockdown**

After delving into respondents' relationships with space, we monitored the evolution of their relationship with time, focusing in particular on their time spent on care, housework, professional activities, virtual social interactions, and leisure activities by employing a battery of questions replicated three times, at the beginning, middle, and end of the lockdown (i.e. survey waves 1, 2, and 3). These data allow us to dress an accurate portrait of

French residents' time-use at home during lockdown. Two main findings clearly emerge: 1. people devoted a lot of energy adjusting to organizing their routines under lockdown and spent more time in home activities in April than in May, and 2. women dedicated more time to more activities than men, picking up the slack in care, housework, and networking activities (see also Bès et al. 2020).

The beginning of the lockdown period saw a spike in the average amount of time caring, working, on the phone, on social media, working out, and watching TV, with a decrease over the first month (Table 3.1). Overall, women declared spending almost 18 hours each day on these activities compared to almost 14 hours for men—a sizable difference. A month later, the number of hours spent on these activities had declined for both women and men to 13 and nearly 12 hours respectively.

Taking a closer look at the time spent on these activities by gender we see that in the first week of April, women spent on average more than two hours caring for children (while men spent less than half of this time), 0.65 hours taking care of dependents (as opposed to 0.49 hours of men), almost two hours on housework (with men spending half of that time on this form of unpaid labor), more than two hours on their professional activity (with men spending one hour more), two and half hours speaking over the phone (with men at one and half hours), more than three hours on social media (with men at half of this), and four hours in front of the TV (with men at a similar level). Other than work, the only activity where men spent slightly more time than women is in athletic activities, with a bit more than one hour per day on average. One month later at the beginning of May, women declared spending less time on housework (-0.76 hours), childcare (-0.45), care of dependents (-0.13), professional activity (-0.27), sport (-0.33), phone conversations (-0.81), and social media (-0.55). A similar decline applies to men but with a less dramatic difference between the start and the end of lockdown. It seems that women (and occasionally men) boosted their efforts on all domestic fronts to compensate for the lack of external welfare services, such as elementary school. Coping with COVID-19 has thus meant extra work, most of it unpaid. The decline in these efforts at home over time may be interpreted either in terms of 'fatigue' or in terms of 'learning:' after an

<footnote n="11">11. These figures refer to the proportion of respondents reporting to have met relatives or friends (apart from the people they live with) 'face-to-face' in the previous two weeks. We cannot rule out that some respondents may interpret 'face-to-face' as taking place through video calls as well.</footnote>
initial strong push, people grew tired and reduced their effort, or they became more efficient in adapting to the 'new normal' of a world in lockdown. Since we do not have comparable data for the pre-lockdown period, we cannot adjudicate between these two interpretations or determine if there are other factors at play.

If we break down activities into unpaid labor (housework, child-care, and care for the dependents), paid labor, networking (time spent over the phone and social media), and leisure (time spent on sport and watching TV) we see some other structural differences between men and women in their use of time. First, both women and men spent 41 to 42 percent of their time doing either paid or unpaid labor (correspondingly, this meant an equal split between genders in time spent on the combination of networking and leisure activities). The key difference lies in the fact that women did more unpaid and less paid work. Moreover, there was an adjustment between unpaid and paid work over time: towards the end of lockdown people appeared to increase

the proportion of their time dedicated to formal labor and to reduce time spent on unpaid labor, suggesting a return towards a 'normal' balance between the two. Second, women spent more time in networking activities than men and devoted a smaller proportion of their time to leisure activities. However, we observe a progressive convergence with women who trimmed time off networking in favor of leisure (though still devoting a significantly smaller amount of their time to it than men).

In a series of pooled regression analyses from three waves, we incorporated a variety of socioeconomic control variables to consider the determinants of time spent on each activity separately and the aggregation of time spent on unpaid work, paid work, networking, and leisure (Table 3.2).

Gender, age, and living with children are key factors in the variation of time spent in unpaid work. While education and income have some impact on childcare, gender is the only variable that significantly affects the time devoted to all unpaid care activities.

<table><caption>Table 3.1. Detailed time use by gender: CoCo survey waves 1 and 3 (April 1-May 6, 2020)</caption><thead><tr><th>Women</th><th>Hours, April 1-8</th><th>Hours, April 29-May 6</th><th>Percent April 1-8</th><th>Percent April 29-May 6</th><th>Delta Over time</th><th>Delta percent</th></tr></thead><tbody><tr><td>Housework</td><td>1.98</td><td>1.22</td><td>11.10%</td><td>9.20%</td><td>-0.76</td><td>-1.90%</td></tr><tr><td>Childcare</td><td>2.28</td><td>1.83</td><td>12.70%</td><td>13.80%</td><td>-0.45</td><td>1.00%</td></tr><tr><td>Taking care of dependents</td><td>0.65</td><td>0.52</td><td>3.60%</td><td>3.90%</td><td>-0.13</td><td>0.20%</td></tr><tr><td>Professional activity</td><td>2.20</td><td>1.93</td><td>12.30%</td><td>14.50%</td><td>-0.27</td><td>2.20%</td></tr><tr><td>Sport</td><td>1.01</td><td>0.68</td><td>5.60%</td><td>5.10%</td><td>-0.33</td><td>-0.50%</td></tr><tr><td>Speaking on phone</td><td>2.57</td><td>1.76</td><td>14.40%</td><td>13.20%</td><td>-0.81</td><td>-1.70%</td></tr><tr><td>Social media</td><td>3.11</td><td>1.85</td><td>17.40%</td><td>13.90%</td><td>-1.26</td><td>-3.50%</td></tr><tr><td>TV</td><td>4.06</td><td>3.51</td><td>22.70%</td><td>26.40%</td><td>-0.55</td><td>3.60%</td></tr><tr><td>Total</td><td>17.86</td><td>13.3</td><td>100.00%</td><td>100.00%</td><td>-4.56</td><td>0.00%</td></tr></tbody></table><table><thead><tr><th>Men</th><th>Hours, April 1-8</th><th>Hours, April 29-May 6</th><th>Percent April 1-8</th><th>Percent April 29-May 6</th><th>Delta Over time</th><th>Delta percent</th></tr></thead><tbody><tr><td>Housework</td><td>0.91</td><td>0.80</td><td>6.40%</td><td>6.70%</td><td>-0.11</td><td>0.10%</td></tr><tr><td>Childcare</td><td>1.10</td><td>0.90</td><td>8.00%</td><td>7.60%</td><td>-0.20</td><td>-0.40%</td></tr><tr><td>Taking care of dependents</td><td>0.49</td><td>0.41</td><td>3.50%</td><td>3.40%</td><td>-0.08</td><td>-0.10%</td></tr><tr><td>Professional activity</td><td>3.17</td><td>2.93</td><td>23.10%</td><td>24.70%</td><td>-0.24</td><td>1.60%</td></tr><tr><td>Sport</td><td>1.10</td><td>0.84</td><td>8.00%</td><td>7.10%</td><td>-0.26</td><td>-0.90%</td></tr><tr><td>Speaking on phone</td><td>1.47</td><td>1.15</td><td>10.70%</td><td>9.70%</td><td>-0.32</td><td>-1.00%</td></tr><tr><td>Social media</td><td>1.59</td><td>1.28</td><td>11.60%</td><td>10.80%</td><td>-0.31</td><td>-0.80%</td></tr><tr><td>TV</td><td>3.87</td><td>3.51</td><td>28.20%</td><td>29.70%</td><td>-0.36</td><td>1.40%</td></tr><tr><td>Total</td><td>13.7</td><td>11.82</td><td>100.00%</td><td>100.00%</td><td>-1.88</td><td>0.00%</td></tr></tbody></table>
<table><caption>Table 3.2. OLS regressions of hours spent in unpaid labor, paid labor, networking, and leisure activities on selected socio-demographic characteristics (pooled data, vce clustered SE)</caption><thead><tr><th></th><th>Unpaid labor</th><th>Paid labor</th><th>Networking</th><th>Leisure</th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>-1.581***<br>(0.384)</td><td>0.579<br>(0.387)</td><td>-0.257<br>(0.389)</td><td>0.175<br>(0.305)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>-1.497**<br>(0.630)</td><td>-0.330<br>(0.520)</td><td>-1.260**<br>(0.588)</td><td>0.233<br>(0.460)</td></tr><tr><td>Woman</td><td>1.134***<br>(0.207)</td><td>-0.066<br>(0.193)</td><td>0.564**<br>(0.220)</td><td>-0.356*<br>(0.195)</td></tr><tr><td>Foreign born</td><td>-0.095<br>(0.324)</td><td>-0.243<br>(0.330)</td><td>-0.048<br>(0.318)</td><td>-0.948***<br>(0.291)</td></tr><tr><td>Living alone</td><td>-0.404<br>(0.318)</td><td>0.239<br>(0.266)</td><td>0.272<br>(0.371)</td><td>0.232<br>(0.326)</td></tr><tr><td>Living with kids</td><td>2.666***<br>(0.309)</td><td>0.152<br>(0.263)</td><td>-0.015<br>(0.294)</td><td>-0.143<br>(0.258)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.290<br>(0.326)</td><td>0.250<br>(0.285)</td><td>-0.071<br>(0.329)</td><td>0.099<br>(0.317)</td></tr><tr><td>Some college (Ref=below High school)</td><td>0.505<br>(0.326)</td><td>0.123<br>(0.266)</td><td>-0.184<br>(0.304)</td><td>-0.314<br>(0.287)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>-0.085<br>(0.342)</td><td>0.793***<br>(0.285)</td><td>-0.541<br>(0.339)</td><td>-1.054***<br>(0.309)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>1.023<br>(0.755)</td><td>-0.084<br>(0.445)</td><td>0.144<br>(0.604)</td><td>0.278<br>(0.572)</td></tr><tr><td>Working (Ref=Retired)</td><td>0.474<br>(0.494)</td><td>3.938***<br>(0.389)</td><td>0.181<br>(0.451)</td><td>-0.365<br>(0.382)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>-0.099<br>(0.645)</td><td>0.333<br>(0.463)</td><td>0.919<br>(0.700)</td><td>-0.497<br>(0.504)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>0.002<br>(0.382)</td><td>0.035<br>(0.274)</td><td>-0.105<br>(0.368)</td><td>0.037<br>(0.335)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>0.389<br>(0.442)</td><td>0.188<br>(0.310)</td><td>-0.164<br>(0.432)</td><td>0.064<br>(0.356)</td></tr><tr><td>Household income €4000 and more (Ref= below €2000)</td><td>-0.374<br>(0.463)</td><td>0.762**<br>(0.318)</td><td>-0.558<br>(0.418)</td><td>-0.194<br>(0.364)</td></tr><tr><td>Financially vulnerable</td><td>0.578<br>(0.505)</td><td>0.244<br>(0.378)</td><td>0.112<br>(0.385)</td><td>0.488<br>(0.398)</td></tr><tr><td>Manager/Professional</td><td>-0.160<br>(0.283)</td><td>0.291<br>(0.233)</td><td>-0.081<br>(0.289)</td><td>-0.149<br>(0.260)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>0.247<br>(0.346)</td><td>-0.183<br>(0.296)</td><td>-0.001<br>(0.353)</td><td>0.146<br>(0.299)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>0.023<br>(0.322)</td><td>-0.140<br>(0.279)</td><td>-0.257<br>(0.341)</td><td>0.154<br>(0.299)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>0.474<br>(0.364)</td><td>-0.258<br>(0.305)</td><td>-0.658*<br>(0.358)</td><td>-0.134<br>(0.331)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>0.460<br>(0.402)</td><td>-1.115***<br>(0.342)</td><td>-0.231<br>(0.334)</td><td>-0.285<br>(0.287)</td></tr><tr><td>coco2 (Apr 15-22 2020)</td><td>-0.067<br>(0.120)</td><td>-0.113<br>(0.079)</td><td>-0.253*<br>(0.133)</td><td>-0.169<br>(0.133)</td></tr><tr><td>coco3 (Apr 29-May 6 2020)</td><td>-0.405***<br>(0.129)</td><td>-0.104<br>(0.095)</td><td>-0.606***<br>(0.130)</td><td>-0.469***<br>(0.125)</td></tr><tr><td>Constant</td><td>2.416***<br>(0.850)</td><td>0.039<br>(0.649)</td><td>4.208***<br>(0.866)</td><td>5.140***<br>(0.653)</td></tr><tr><td>Observations</td><td>1,917</td><td>1,919</td><td>1,921</td><td>1,918</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
This result aligns with classic time-use studies, which find that women spend more time on housework, childcare, and taking care of dependents (Sullivan 1997).

Age has a more varied effect than gender on unpaid activities with those in our youngest age band (people under the age of 40, and mostly in their thirties) spending much more of their time on them than the rest of the population. This proves to be primarily due to childcare effects (with people aged 60 and above spending significantly more time caring for dependents). Living with kids is significantly associated with housework and it naturally implies a dramatic increase in the amount of time spent on childcare. People with a medium-high level of education and income (earning between €3,000 and €3,999) also spend significantly more time taking care of their children. This is perhaps a reflection of their 'concerted cultivation' style of parenting (Lareau 2003). Or from another angle, being slightly less engaged in professional activities during the lockdown may have permitted this group to spend more time with their children.

People with the highest educational and income levels and those living in larger spaces seem to have dedicated more time to paid work than others. This finding echoes some of the discussion that developed around the importance of having more living space during lockdown in continuing with a more 'normal' way of life (Biland-Curinier 2020). The following quote from one of our diaries illustrates the satisfaction associated with the privilege of having plenty of space at home:

> When we bought the house five years ago, I knew that I would no longer have access to a gym because our town doesn't have one and because I couldn't include a gym membership in my expenses. So instead, at every birthday and Christmas, family and friends of mine gifted me different pieces of equipment so that I could build up my own set little by little. My husband built my gym in the basement. Yesterday I beat my record and did 11km in 1 hour on my elliptical, then did some jump rope right after. Music from Rocky was blasting through the house all morning 'cause it's a true personal VICTORY!<fnref n="12" />

Regarding networking activities (i.e. time spent on the phone and on social media), the only significant difference is related to gender, and much more variation appears in the use of social media across socio-demographic variables. Overall, Facebook is by far the most commonly used platform. Older people, the most highly educated, those in the highest income brackets, and those living in rural areas use social media for less time, while the unemployed dedicate much more of their day to them. In terms of leisure activities, the foreign born and highly educated dedicate a significantly lower amount of time to them. In addition, women and the unemployed spent significantly less time on sport activities.

### 3.4. At home with the children: Gender and tensions

For parents, a major impact on the daily routine was the closure of schools and the resulting move to distance learning. Between April 1 and May 6, women in our survey reported spending dramatically more time than men supervising their children's schoolwork. The gender gap in this regard was over 40 percentage points at the beginning of lockdown with a slight narrowing of this gap to 26 percentage points by the end of the period.¹³

In Table 3.3 we explore heterogeneity in homeschooling supervision and general childcare (i.e. daily time spent with children).¹⁴ Both forms of activities are more likely and frequent as we move up the social ladder. Managers and Professionals are significantly more likely to supervise and assist with their children's schoolwork. The highest earners (household income over €4,000 per month) also spent much more time (a 20 percentage point difference) supervising their children's remote

n'en propose pas, et je ne pouvais pas inclure un abonnement à mes charges. Ainsi, amis et famille, à chaque anniversaire et noël [sic] ont rempli ma cagnotte afin que je puisse petit à petit m'équiper à domicile. Mon mari m'a construit ma salle de sport au sous sol. Hier j'ai battu mon record, j'ai réussi à faire 11 km en 1h sur mon elliptique, puis enchaîner avec de la corde à sauter. La musique de Rocky a résonné toute la matinée dans la maison car c'est une vraie VICTOIRE personnelle!

13. These gaps remained statistically significant at the p<0.05 level, and for the rest of this section, all reported differences are statistically significant at this level, unless otherwise indicated.

14. Note that the models also include people without children at home, who may still be involved in virtual activities with them. More precisely, the first model excludes respondents who said that they were 'not concerned' by the question on children's schooling, regardless of their co-residence with children or not.

<footnote n="12">12. 'Lorsque nous avons acheté la maison il y a 5 ans, je savais que je n'aurai plus accès à, [sic] une salle de sport, car nous notre commune</footnote>
<table><caption>Table 3.3. Logit and OLS regressions of daily school work supervision and hours spent in childcare on selected socio-demographic characteristics (pooled data, vce clustered SE)</caption><thead><tr><th></th><th>Daily school work supervision (logit)</th><th>Daily hours spent in childcare (OLS)</th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>-0.658*<br>(0.351)</td><td>-1.977***<br>(0.314)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>-0.761<br>(1.022)</td><td>-2.199***<br>(0.361)</td></tr><tr><td>Woman</td><td>0.966***<br>(0.290)</td><td>0.348**<br>(0.137)</td></tr><tr><td>Foreign born</td><td>0.404<br>(0.614)</td><td>-0.197<br>(0.223)</td></tr><tr><td>Living alone</td><td>-0.204<br>(0.678)</td><td>0.017<br>(0.157)</td></tr><tr><td>Living with kids</td><td>1.381**<br>(0.671)</td><td>2.442***<br>(0.189)</td></tr><tr><td>High school (Ref=below High school)</td><td>1.397***<br>(0.463)</td><td>0.188<br>(0.202)</td></tr><tr><td>Some college (Ref=below High school)</td><td>0.872*<br>(0.451)</td><td>0.442**<br>(0.204)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.437<br>(0.448)</td><td>0.257<br>(0.233)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>-0.112<br>(0.985)</td><td>0.033<br>(0.374)</td></tr><tr><td>Working (Ref=Retired)</td><td>-0.418<br>(0.849)</td><td>-0.270<br>(0.212)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>1.287<br>(1.243)</td><td>-0.361<br>(0.450)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>0.551<br>(0.468)</td><td>0.148<br>(0.199)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>0.456<br>(0.499)</td><td>0.551**<br>(0.251)</td></tr><tr><td>Household income €4000 and more (Ref=below €2000)</td><td>0.797*<br>(0.482)</td><td>0.226<br>(0.272)</td></tr><tr><td>Financially vulnerable</td><td>0.228<br>(0.574)</td><td>0.249<br>(0.297)</td></tr><tr><td>Manager/Professional</td><td>0.677*<br>(0.360)</td><td>-0.101<br>(0.215)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.104<br>(0.503)</td><td>0.149<br>(0.235)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>0.524<br>(0.471)</td><td>0.073<br>(0.226)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>1.480***<br>(0.522)</td><td>0.500*<br>(0.255)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>0.993***<br>(0.372)</td><td>0.580**<br>(0.285)</td></tr><tr><td>coco2 (Apr 15-22 2020)</td><td>-0.051<br>(0.131)</td><td>-0.061<br>(0.065)</td></tr><tr><td>coco3 (Apr 29-May 6 2020)</td><td>-0.019<br>(0.159)</td><td>-0.184***<br>(0.062)</td></tr><tr><td>Constant</td><td>-3.061**<br>(1.524)</td><td>1.553***<br>(0.476)</td></tr><tr><td>Observations</td><td>582</td><td>1,919</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
learning when compared to respondents living in households making less than €2,000 per month. Respondents with higher education levels also devoted more time to helping their kids with remote learning. Both types of activities were also more likely to occur in more cramped apartments; spatial contiguity ‘forced’ parents and children together, so to speak. In addition, people living in rural areas declared having more involvement with children than respondents from more urban settings.

With our panel, we repeatedly monitored the share of respon-
dents experiencing family tensions during lockdown and imme-
diately afterwards. Looking at those who had declared experi-
encing these tensions at least ‘sometimes,’ we find an inverted-U
shape. Tensions were lower two weeks after the beginning of
the lockdown, subsequently increasing in mid-April—reaching
the peak for women, but not for men who hit their peak two
weeks later—and declining with the end of lockdown. When
performing a regression with all typical socio-demographic
controls, gender does not seem to be a significant explanatory
factor in the share of family tensions reported. As anticipated in
Chapter 2, the key factors associated with a higher likelihood
of experiencing family tensions during and after lockdown are
instead related to age (tensions being constantly higher among
younger respondents), living in a space smaller than 25m² per
person, and especially living with kids.

**3.5. Connected to the world from home: Digital inequalities
during the lockdown**

Scholars have argued for the past decade that ‘first-level’ digital divides like computer or Internet access were decreasingly im- important matters in the face of rising ‘second-level’ (mechanisms of digital inequalities) or ‘third-level’ (outcomes) digital divides (van Deursen & Helsper 2015). However, lockdown made basic Internet access a key to survival for people throughout France and beyond. Whether for ordering groceries and finding infor- mation, or to be able to work and learn, digital technologies became a tool for connection to the outside world during this period.

Basic Internet access in France mirrors the situation in much
of Europe and North America: 88 percent of French residents
have an Internet connection, though that is not always at home

(CRÉDOC 2019). While high speed Internet access is on the rise in general, still only 30 percent of the population has a fiber optic connection and only 65 percent of those with an Internet connection have one that is strong enough to support watching videos (Ibid). However, 85 percent of our sample said that they had good Internet access. Given that an Internet connection is a precondition for panel participation, it is not surprising that this proportion is higher than the national average. Nonetheless, four main factors serve to stratify this first level digital divide: income, education, geography, and national origin. First, 91 percent of respondents whose household incomes are above €4,000 per month report that they have good Internet connection, while only 74 percent of respondents with household incomes less than €2,000 per month declare the same. The gap in educational levels is slightly narrower, with a 10 percentage point difference between respondents with a graduate degree and those without a high school degree. Similar differences exist between urban and rural areas, which aligns with other research (Ibid; Pasquier 2018). Foreign-born status proved similar in size to the income gap, with 87 percent of French-born respondents reporting that they had good Internet access (just above the national average) as opposed to only 71 percent of those born elsewhere.

In the open-ended questions, one of which was devoted to In-
ternet issues across 25 of the daily journal responses, panelists
living without consistent Internet access often reported having
limited data allowance, dealing with a spotty Internet connec-
tion, or simply reporting that 'it didn't work,' with one respon-
dent getting excited that 'for once, no connection or speed
problems.'<fnref n="15" /> Other problems that respondents reported were
related to websites or printers.

Simply having Internet access may not be sustainable during a lockdown if multiple people in a household are also vying for a limited number of desktops, laptops, and smartphones. To look at this in more detail, we evaluated how many computers were in each household and found that a full 10 percent of people whose household income is less than €2,000 per month had no computers at home during lockdown. At the same time, people

<footnote n="15">15. ‘Pour une fois, aucun problème de connexion ou vitesse.’</footnote>
who have a graduate degree were dramatically more likely to have more than one computer in their household (65 percent), while only 41 percent of those with less than a high school education reported that was the case.

In Table 3.4, we show that this basic first-level digital divide persisted even when accounting for a variety of factors, according to our ordered logistic regression model of computers per household member on selected socio-demographic characteristics. First, people without children were much more likely to have access to more devices. Because families with children reported having fewer devices per person, we can see how digital technology inequalities exacerbated educational and childcare inequalities, especially when these devices are required to play the role of teacher or babysitter. We also found how important one's social class background is in having access to gadgets. People who had higher incomes and advanced degrees had more devices at their disposal, and this was also true for people whose living quarters were larger and in urban areas.

### 3.6. Conclusion

With the 'Restez chez vous !' slogan quickly taking over as one of the most visible and important messages of lockdown, the home became the hub of daily activities. And while outings were still permitted for reasons deemed necessary, they were limited both spatially and temporally. We have found that the French were widely compliant with these measures at the start, only to take a more relaxed approach as time went on. In spite of this, social life being confined to the home has profound implications on gender relations and digital inequalities.

The legal requirement to stay home meant an increased load of domestic responsibilities, such as meeting the new needs of childcare and education during schooling hours and cooking more meals while restaurants were closed. A key takeaway from our analyses is that women became society's 'shock absorbers of last resort' (Elson 2002), performing additional care work with children and the most vulnerable family members in order to compensate for the shuttering of services like schools and childcare centers. On top of this, domestic work fell largely on women's shoulders.

<table><caption>Table 3.4 Ordered logit regression of number of computers available at home per household member (excluding children younger than 5) on selected socio-demographic characteristics (CoCo Survey wave 1)</caption><tr><td>Age 40-60 (Ref=below 40 years)</td><td>0.197<br>(0.275)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>0.305<br>(0.414)</td></tr><tr><td>Woman</td><td>0.056<br>(0.177)</td></tr><tr><td>Foreign born</td><td>-0.428<br>(0.307)</td></tr><tr><td>Living alone</td><td>2.990***<br>(0.469)</td></tr><tr><td>Living with kids</td><td>-0.957***<br>(0.213)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.047<br>(0.247)</td></tr><tr><td>Some college (Ref=below High school)</td><td>0.255<br>(0.260)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.596**<br>(0.293)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>-0.035<br>(0.475)</td></tr><tr><td>Working (Ref=Retired)</td><td>0.329<br>(0.360)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>0.389<br>(0.541)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>0.151<br>(0.281)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>0.163<br>(0.300)</td></tr><tr><td>Household income €4000 and more (Ref=below €2000)</td><td>0.751**<br>(0.316)</td></tr><tr><td>Financially vulnerable</td><td>-0.210<br>(0.358)</td></tr><tr><td>Manager/Professional</td><td>0.163<br>(0.238)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.174<br>(0.323)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>-0.438<br>(0.303)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.635**<br>(0.318)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>-0.841***<br>(0.246)</td></tr><tr><td>Observations</td><td>649</td></tr></table>

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
The gender differences in our results are highly significant: during lockdown, women spent much more time caring, doing housework and maintaining social ties, while men were more likely to dedicate themselves to leisure activities, such as working out. This appears to align closely with classic time-use survey analyses across the Western world (Craig 2006; Mattingly & Bianchi 2003; Sullivan 1997). Paraphrasing a Led Zeppelin hit from the 1970s, in the calm or in the storm, 'the song remains the same'...or perhaps gets worse for women who help to absorb the shock of crisis.

On a more general note, the pandemic and subsequent lockdown complicated the 'quiet revolution' of women's participation in the economy (Goldin 2006) by turning the home back into the center of 'social reproduction' activities, reminiscent of a bygone era. This further boosted women's burden of housekeeping and care activities, which had already been deepened by welfare state retrenchment (Ferragina 2019). It appears that the COVID-19 crisis has reminded French society that in a paternalistic environment still dominated by male-breadwinner norms, it is up to women to cover the gap when economic activities and outside help with childcare and other home activities are disrupted.

The other societal transformation that has reshuffled the domestic space is in the digital realm. On the one hand, advances in digital technologies enabled schooling, work, shopping, and socializing to continue during the lockdown, a stark contrast to what would have been possible a decade ago, let alone during the 1918 influenza pandemic. Some scholars have celebrated this technological revolution (Castells 2010) as key to networked individualism (Rainie & Wellman 2012), but the reality is that the poor and working-class are not able to control their digital means of production in the best of times (Schradie 2011; Schradie 2020), and certainly not in the worst of times during a pandemic. Many demonstrated that they were not able to maintain their work, school, social or shopping activities fully online, often having to share a limited number of devices or wrestling with a weak Internet connection. Despite claims that digital technology has been a step forward in changing social life as we know it, during the disruption caused by a pandemic, lack of equal access has only exacerbated existing inequalities.

# Chapter 4. Feeling: Health and Well-Being

## 4.1. Introduction

For individuals' health and well-being, the current pandemic has proven to be unlike any other moment of crisis in recent memory (Brooks et al. 2020). Likewise, while some sentiments have taken on a predictable nature—for example, larger dips in well-being among those closer to severe outbreaks (Yang & Ma 2020)—many outcomes have been unexpected and sometimes surprising, including increases in overall subjective well-being in the general population (Recchi et al. 2020a). Before moving to our longitudinal results, it is first necessary to illustrate how health and well-being are measured in the literature, and the social conditions that affect these measures, particularly during moments of disaster.

Psychology has long been interested in self-reported assessments of well-being and health (Diener 1984). Concerned initially with how people conceive of their lived experiences and why an event is considered positively (Ibid, 542), measures of well-being have also figured in the sociological debate of how to track the impact of disasters and the community altruism or tensions that follow (Tierney 2007). Indeed, in the extant literature, using a form of well-being or health assessment in order to better understand the social effects of disasters on individuals is widespread, and ranges from analyzing happiness and well-being following natural disasters (Calvo, Arcaya, Baum, Lowe & Waters 2015; Uchida, Takahashi & Kawahara 2014), to looking at subjective well-being in the aftermath of economic crises (Hald Andersen 2009). Findings often point to the scope and magnitude of disasters as explanatory factors of the reported experiences of affected individuals: one of the most common conclusions is that people closer to the epicenter of—or more affected by—a disaster will experience greater declines in their health and well-being (Sastry & Van Landingham 2009; Calvo et al. 2015), and those farther away or less affected will show milder declines (Yang & Ma 2020), or even report a slight increase in satisfaction with their personal conditions (Uchida et al. 2014), benefiting from what we have called 'the eye of the hurricane' paradox (Recchi et al. 2020a).
Measuring well-being in the current pandemic gives us the chance to break new ground given that the scope of the lockdown and subsequent economic effects have, in many ways, dwarfed any other disaster in recent history. What happens to people's reported health and well-being when a major health threat moves directly into local communities and is no longer a distant issue? Who is most vulnerable to shifts in these measures?

**4.2. The prevalence of the virus: Increasing inequalities over time**

As the spread of COVID-19 escalated in France and lockdown began, the disease swiftly became a part of people's everyday lives, moving from media stories directly into their communities as reports of knowing someone who had been infected grew. In our first survey two weeks into the lockdown, 41 percent of respondents already directly knew someone who had contracted the virus. Our sample proves to be a good thermometer of the spread of the virus itself, complementing epidemiological evidence, which was—particularly in France—limited by the reduced number of tests during the lockdown period.<fnref n="16" /> As general practitioners were instructed to tell potential patients with mild symptoms to self-quarantine, the proportion of people suspecting a COVID-19 infection ('Do you think you have or have had COVID-19?') can serve as a more realistic estimate than test-based measurements during this period. Overall, over our five survey waves, this proportion went up from 7 to 9 percent. As it turns out, our self-reported measure of the prevalence of COVID-19 in France is only marginally higher than epidemiological estimates.<fnref n="17" />

Table 4.1 details the most at-risk social groups. As in official data—according to which 54 percent of those infected between March and June 2020 were men—the risk of contagion was significantly lower for women in our sample, and progressively so over time (analysis not shown). In fact, the highest risk factor, age, does not show significance in our data because one of the

few limitations of the ELIPSS panel is that it does not cover retirement homes, as well as hospitals, prisons and the homeless (see Chapter 1). While people in such institutions are statistically a small number, they have been disproportionately affected by the pandemic. Moreover, elderly people who became infected were far more likely to be hospitalized, and thus may be missing from the sample, causing bias in the age results. In fact, we record a significantly higher risk for people with a lower level of education (but not the lowest), the unemployed, the inactive (also including university students), and the financially vulnerable. Interestingly however, when it comes to income levels, we do not find a linear relationship. The risk is lowest for those in the third quartile of the income distribution (almost significant statistically), maybe due to their capacity and incentives to stay home, with one's employment situation playing a major role in this capacity (see Chapter 2).

This closely follows the notion that those in lower income brackets have significantly less choice in where to work (Wheatley 2017; Felstead et al. 2002), and the need to continue commuting to work among the lower income groups appears to have been relatively higher with the lockdown measures in France (Lambert et al. 2020a). Whereas those placed on the lowest part of the income distribution may work in occupations that required physical presence during the lockdown, those in the middle-upper quartile may have been dictated to work from home or furloughed. Comparatively, in fact, the risk is higher (but not significant) for respondents in the top quartile of the income distribution. Their generally larger social capital may be a health risk factor (Lin 2000; Savage et al. 2013). Overall, the risk of contracting the virus is likely to reflect different levels of exposure to social contact in the workplace or in public, like essential work or frequent use of public transportation.

Another major source of infection inequality was linked to geography. We have already noted a strong Paris-centric effect where the likelihood of infection was higher in the French capital region (Île-de-France) than anywhere else in the country (Recchi et al. 2020b). This is confirmed by macro data on the spread of the virus across French regions (Deshaies 2020). Our model shows that geographical differences do not arise from a compositional effect (i.e., the profile of Parisian residents). However, in

<footnote n="16">16. Only after the end of the lockdown, all suspected cases were subject to testing (Santé Publique France 2020, 7).</footnote>

<footnote n="17">17. On June 30, the official test-based rate of the population of mainland France ever infected is 0.3 percent: https://www.data.gouv.fr/fr/reuses/covid-19-taux-de-population-infectee-par-pays/. Epidemiologists estimate a real rate between 2.8 and 7.2 per cent (Salje et al. 2020).</footnote>
<table><caption>Table 4.1. Logit regression of self-reported Covid-19 infection on selected socio-demographic characteristics (pooled data, vce clustered SE)</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>0.271<br>(0.460)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>0.487<br>(0.712)</td></tr><tr><td>Woman</td><td>-0.621*<br>(0.326)</td></tr><tr><td>Foreign born</td><td>-0.528<br>(0.576)</td></tr><tr><td>Living alone</td><td>0.526<br>(0.461)</td></tr><tr><td>Living with kids</td><td>0.232<br>(0.397)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.891*<br>(0.476)</td></tr><tr><td>Some College (Ref=below High school)</td><td>0.040<br>(0.531)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.154<br>(0.645)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>1.684**<br>(0.778)</td></tr><tr><td>Working (Ref=Retired)</td><td>0.602<br>(0.617)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>1.706**<br>(0.817)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>0.042<br>(0.589)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>-0.844<br>(0.577)</td></tr><tr><td>Household income €4000 and more (Ref=below €2000)</td><td>0.140<br>(0.493)</td></tr><tr><td>Financially vulnerable</td><td>1.009**<br>(0.456)</td></tr><tr><td>Manager/Professional</td><td>-0.145<br>(0.565)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.962**<br>(0.435)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>-1.011**<br>(0.432)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.661<br>(0.477)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>0.128<br>(0.458)</td></tr><tr><td>coco2 (Apr 15-22 2020)</td><td>-0.097<br>(0.150)</td></tr><tr><td>coco3 (Apr 29-May 6 2020)</td><td>0.175<br>(0.124)</td></tr><tr><td>coco4 (May 13-20 2020)</td><td>0.176<br>(0.149)</td></tr><tr><td>coco5 (May 27-June 4 2020)</td><td>0.166<br>(0.138)</td></tr><tr><td>Constant</td><td>-3.059***<br>(0.980)</td></tr><tr><td>Observations</td><td>3,172</td></tr></tbody></table>
<table><caption>Table 4.2. OLS regressions of lockdown-related stress scale on selected socio-demographic characteristics (pooled data, vce clustered SE)</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>0.133<br>(0.292)</td><td>0.146<br>(0.293)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>-0.029<br>(0.422)</td><td>-0.004<br>(0.420)</td></tr><tr><td>Woman</td><td>-0.058<br>(0.184)</td><td>-0.051<br>(0.184)</td></tr><tr><td>Foreign born</td><td>-0.172<br>(0.345)</td><td>-0.187<br>(0.341)</td></tr><tr><td>Living alone</td><td>1.043***<br>(0.280)</td><td>1.042***<br>(0.280)</td></tr><tr><td>Living with kids</td><td>0.017<br>(0.229)</td><td>0.006<br>(0.229)</td></tr><tr><td>High school (Ref=below High school)</td><td>-0.384<br>(0.267)</td><td>-0.378<br>(0.268)</td></tr><tr><td>Some College (Ref=below High school)</td><td>-0.247<br>(0.266)</td><td>-0.273<br>(0.265)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>-0.721**<br>(0.312)</td><td>-0.748**<br>(0.317)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>-0.046<br>(0.477)</td><td>-0.057<br>(0.475)</td></tr><tr><td>Working (Ref=Retired)</td><td>-0.245<br>(0.348)</td><td>-0.181<br>(0.349)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>-0.174<br>(0.554)</td><td>-0.103<br>(0.546)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>0.256<br>(0.284)</td><td>0.200<br>(0.284)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>-0.148<br>(0.307)</td><td>-0.221<br>(0.305)</td></tr><tr><td>Household income €4000 and more (Ref=below €2000)</td><td>-0.030<br>(0.339)</td><td>-0.106<br>(0.339)</td></tr><tr><td>Financially vulnerable</td><td>0.339<br>(0.400)</td><td>0.299<br>(0.408)</td></tr><tr><td>Manager/Professional</td><td>0.292<br>(0.247)</td><td>0.319<br>(0.254)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.017<br>(0.303)</td><td>0.027<br>(0.302)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>-0.086<br>(0.283)</td><td>-0.021<br>(0.283)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.416<br>(0.317)</td><td>-0.365<br>(0.315)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>0.741***<br>(0.281)</td><td>0.724**<br>(0.287)</td></tr><tr><td>Left house once per week or less (Ref=Never)</td><td></td><td>-0.681**<br>(0.338)</td></tr><tr><td>Left house once every two or three days (Ref=Never)</td><td></td><td>-0.326<br>(0.363)</td></tr><tr><td>Left house every day or almost every day (Ref=Never)</td><td></td><td>-0.668*<br>(0.369)</td></tr><tr><td>Working at workplace, job not compatible with telework</td><td></td><td>-0.076<br>(0.304)</td></tr><tr><td>Constant</td><td>5.234***<br>(0.605)</td><td>5.741***<br>(0.694)</td></tr><tr><td>Observations</td><td>3,179</td><td>3,149</td></tr></tbody></table>

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
our sample the lower prevalence of the virus in rural areas is not statistically significant. We also disprove the impact of a commonly imagined risk factor, population density in the household, which does not show up significantly as a predictor of a reported COVID-19 infection, though it may well be a source of additional psychological burden during the lockdown as we will see in the following analyses.

### 4.3. Subjective well-being: Up for many, but not equally

Our study digs deeper into the socio-psychological impact of the epidemic and the lockdown period. Did the latter, in particular, provoke specific anxieties among French residents? We investigated the issue with a simple, direct question: 'Does the lockdown take a toll on you?' Possible answers ranged from 'not at all' to 'immensely' on a 0-10 scale. The average score was 5.08 after two weeks, increasing further during lockdown, and reaching 5.46 in early May, only to drop to 4.54 in a retrospective assessment the week after the end of strict lockdown measures. The normal distribution of scores in fact conceals some heterogeneity among respondents (Table 4.2). The four constantly strongest predictors of subjective lockdown-related stress are education, living alone, never going out at least once per week, and having little living space at home. These last three factors indicate the vital importance of physical space in maintaining psychological balance. Immobility proves psychologically harder to bear when alone, staying in for long periods, and doing so in smaller living quarters (see also Lambert et al. 2020b). Once we control for these characteristics, the negative impact of being locked down in Paris declines and loses statistical significance.

Initially, the stress was significantly greater for people aged 40 to 60, but this effect disappeared over time. To a lesser extent, the mitigating effect of education follows the same declining trajectory. Still, people with a graduate degree are significantly less likely to declare a psychological burden due to lockdown. Since we control for income and occupation (which have no significant impact), we may conjecture that higher education levels provide cognitive tools to master the unprecedented situation, such as the ability to confidently access and interpret reliable information on the risks and perspectives of the pandemic (Lambert et al. 2020c). This closely follows the conclusion that

confidence in the accuracy of the information one holds has been a strong predictor of lower levels of reported strain from COVID-19 lockdowns (Yang & Ma 2020).

The longitudinal dimension of the ELIPSS panel allows us to better comprehend just how disruptive this event has been. We analyze the impact of the situation on self-assessed psychological states that had already been measured in the ELIPSS yearly surveys from Spring 2017, 2018, and 2019, namely nervousness, discouragement, relaxation, feeling defeated, happiness, and loneliness. We add to this list a seventh variable which touches upon respondents' self-assessment of their general health (Table 4.3) in order to create a more accurate measure of Subjective Well-Being (Cf. Diener, Emmons, Larsen & Griffin 1985). These factors were rescaled from 0 (lowest) to 1 (highest positive affect). A factor analysis shows that a single factor accounts for an overwhelming part of variance (Table 4.4). We used factor scores of this prevailing factor as weights of the seven aforementioned items to build a Subjective Well-Being Index (SWBI) that incorporates evaluative and emotive measures of happiness, along with reported physical health (Diener 1984).

Overall, respondents' self-assessments of their psychological states in the lockdown period vary substantially compared to beforehand, when they were rather stable year after year. The mean of each indicator increased by about 10 percent during lockdown, with marginal variations across waves and a further surge at the end of lockdown. The only temporary exception is self-reported happiness, which declined around the time of the first survey wave (early April, 2020), only to rise above the pre-COVID-19 level in the following waves; the 'happiness' indicator is also that with the lowest increase overall. Our SWBI changes likewise: while it wavered between .64 and .65 in the three preceding years, it increased to .69 in early April, 2020 and reached .72 by the end of May. Following the shock of the lockdown, as people find out that they are capable of navigating the troubled times of the pandemic, they better appreciate their current conditions. A relatively stable personal situation in light of a world turned upside down can be psychologically comforting, as was found after the 9/11 terrorist attacks in 2001 in the US (Claassen et al. 2010) or the Japanese earthquake and tsunami of 2011 (Uchida & al. 2014). In another paper we named this 'the eye of
<table><caption>Table 4.3. Items used for the construction of the Subjective Well-Being Index</caption><thead><tr><td>Indicator</td><td>Question</td><td>Scale</td></tr></thead><tbody><tr><td>Nervousness</td><td>In the last two weeks, have there been times when you felt very nervous?</td><td>1-5 (always to never)</td></tr><tr><td>Discouragement</td><td>In the last two weeks, have there been times when you felt discouraged/low?</td><td>1-5 (always to never)</td></tr><tr><td>Relaxation</td><td>In the last two weeks, have there been times when you felt relaxed?</td><td>1-5 (never to always)</td></tr><tr><td>Defeatedness</td><td>In the last two weeks, have there been times when you felt defeated?</td><td>1-5 (always to never)</td></tr><tr><td>Happiness</td><td>In the last two weeks, have there been times when you felt happy?</td><td>1-5 (never to always)</td></tr><tr><td>Loneliness</td><td>In general, would you say that you feel lonely?</td><td>1-5 (always to never)</td></tr><tr><td>General health</td><td>Would you say that, overall, your health is: ...</td><td>1-5 (very bad to very good)</td></tr></tbody></table>

<table><caption>Table 4.4. Principal factor analysis of the seven items of the Subjective Well-Being Index</caption><thead><tr><td>Factor</td><td>Eigenvalue</td></tr></thead><tbody><tr><td>Factor1</td><td>3.20205</td></tr><tr><td>Factor2</td><td>0.27718</td></tr><tr><td>Factor3</td><td>0.10386</td></tr><tr><td>Factor4</td><td>-0.01795</td></tr><tr><td>Factor5</td><td>-0.11499</td></tr><tr><td>Factor6</td><td>-0.13412</td></tr><tr><td>Factor7</td><td>-0.19224</td></tr></tbody></table>

<table><caption>Factor loadings and scoring coefficients of Factor 1 (method = regression)</caption><thead><tr><td>Variable</td><td>Factor loadings</td><td>Scoring coefficients</td></tr></thead><tbody><tr><td>Nervousness</td><td>0.705</td><td>0.158</td></tr><tr><td>Discouragement</td><td>0.806</td><td>0.268</td></tr><tr><td>Relaxation</td><td>0.765</td><td>0.228</td></tr><tr><td>Defeatedness</td><td>0.785</td><td>0.231</td></tr><tr><td>Happiness</td><td>0.696</td><td>0.184</td></tr><tr><td>Loneliness</td><td>0.451</td><td>0.078</td></tr><tr><td>General health</td><td>0.404</td><td>0.064</td></tr></tbody></table>

N=8,459

the hurricane' paradox: being untouched in the midst of a storm provides a sense of relief (Recchi et al. 2020a).

We offer three more general interpretations for this paradox. The first one—inspired by rational choice—is that subjective well-being is a positional good, which consequently reflects the Ego's distance from the (perceived) average well-being of the rest of society. The second one comes from the psychological literature: an individual is likely to report higher levels of subjective well-being if she feels that her condition is more favorable to those around her (Schwarz & Strack 1999). The third one, inspired by Durkheim's *Suicide* (1897), is that tragic events—wars, revolutions, or epidemics—trigger major and widespread 'social commotions,' which bring people emotionally closer and strengthen them psychologically. Unfortunately, we are not in a position to adjudicate among these alternative readings, which may not be mutually exclusive.

If we regress these indicators individually on our independent variables, we find many regularities. The bulk of predictors are significant before, during, and after the lockdown. Living alone is consistently associated with feeling defeated, discouraged, unhappy, nervous, less relaxed and (unsurprisingly) lonelier. Reporting poor subjective well-being is also recurrently higher for those who are most financially vulnerable, the unemployed (see Zhang et al. 2020), and women (but not for happiness and self-perceived health), all of which may be interrelated in this particular crisis (Cf. Lambert et al. 2020a). Heterogeneity in the level of happiness is somewhat different from the other components of subjective well-being, which may be due to its closer association with emotive measures of well-being that tend to fluctuate more readily than evaluative measures (Kahneman & Riis 2005). In addition to people living alone and in smaller living spaces, the unemployed, the financially vulnerable, and individuals between the ages of 40 and 60 are significantly less happy. Before the start of the pandemic, but not after, income was also
<table><caption>Table 4.5. OLS regression of Subjective Well-Being Index on selected socio-demographic characteristics for the 2017/2018/2019 Annual Surveys (Enquête Annuelle) and the five CoCo survey waves (April-June 2020)</caption><thead><tr><th></th><th>EA Average</th><th>CoCo Average</th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>-0.020<br>(0.018)</td><td>-0.019<br>(0.020)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>0.006<br>(0.028)</td><td>0.015<br>(0.029)</td></tr><tr><td>Woman</td><td>-0.037***<br>(0.011)</td><td>-0.037***<br>(0.012)</td></tr><tr><td>Foreign born</td><td>0.001<br>(0.021)</td><td>-0.009<br>(0.022)</td></tr><tr><td>Living alone</td><td>-0.052***<br>(0.018)</td><td>-0.081***<br>(0.018)</td></tr><tr><td>Living with kids</td><td>0.020<br>(0.014)</td><td>0.008<br>(0.015)</td></tr><tr><td>High school (Ref=below High school)</td><td>-0.008<br>(0.017)</td><td>0.011<br>(0.018)</td></tr><tr><td>Some college (Ref=below High school)</td><td>0.010<br>(0.017)</td><td>-0.005<br>(0.018)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.033*<br>(0.019)</td><td>0.026<br>(0.020)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>-0.058*<br>(0.032)</td><td>0.003<br>(0.033)</td></tr><tr><td>Working (Ref=Retired)</td><td>-0.032<br>(0.024)</td><td>-0.002<br>(0.025)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>-0.119***<br>(0.037)</td><td>-0.094**<br>(0.039)</td></tr><tr><td>Household income €2000-€2999 (Ref=below €2000)</td><td>0.034*<br>(0.018)</td><td>-0.001<br>(0.019)</td></tr><tr><td>Household income €3000-€3999 (Ref=below €2000)</td><td>0.025<br>(0.020)</td><td>0.007<br>(0.021)</td></tr><tr><td>Household income €4000 and more (Ref=below €2000)</td><td>0.026<br>(0.021)</td><td>0.003<br>(0.025)</td></tr><tr><td>Financially vulnerable</td><td>-0.082***<br>(0.023)</td><td>-0.078***<br>(0.024)</td></tr><tr><td>Manager/Professional</td><td>-0.010<br>(0.016)</td><td>-0.013<br>(0.017)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>0.026<br>(0.019)</td><td>0.039*<br>(0.021)</td></tr><tr><td>Urban area 2000-0.2m inhabitants (Ref=Paris)</td><td>-0.008<br>(0.019)</td><td>0.023<br>(0.020)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.000<br>(0.020)</td><td>0.017<br>(0.021)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>-0.046**<br>(0.018)</td><td>-0.031<br>(0.019)</td></tr><tr><td>Constant</td><td>0.684***<br>(0.039)</td><td>0.726***<br>(0.041)</td></tr><tr><td>Observations</td><td>649</td><td>649</td></tr></tbody></table>

a significant predictor of happiness and in this unique regard, the lockdown period was equalizing. In contrast, differences in levels of happiness were not significantly associated with place of residence, whereas during the lockdown, residents of Paris were found to be significantly less happy than those residing

anywhere else in France. Differences in self-reported health are also recurrently associated with income, holding a graduate-level degree, and employment status (the employed before lockdown reported a significantly higher sense of general health than those who were unemployed). This is consistent
with other work done on the effects of COVID-19 lockdowns outside of France (Zhang et al. 2020).

All these nuances are captured synthetically by the SWBI (Table 4.5). Essentially, while the index score increases with the lockdown, its heterogeneity is rather constant before and after the beginning of the pandemic. Across the board, women, the unemployed, and the most financially vulnerable systematically report lower levels of well-being. Before the lockdown, higher education levels and income had a small effect, which became insignificant thereafter. Surprisingly, the same is true for flat size, as in principle the lockdown might have exacerbated, not reduced, the constraints of a small living space. In fact, a post-COVID-19 amplifier of well-being is living outside Paris, with this being significantly true for residents of other major urban areas.<fnref n="18" />

### 4.4. Good and bad moments during lockdown

Throughout the survey, we analyzed various measures of self-reported well-being, not only physically but also emotionally. As noted, we also asked respondents to provide open-ended responses to how they were feeling in the form of a regular short journal entry. Preliminary findings reveal the challenges and opportunities that the lockdown period presented.

The most frequently cited topic of both 'bad' and 'good' moments were 'children' across all respondents, even including those without kids. Children were often associated with challenges like struggling to figure out how to supervise schooling and working from home at the same time. One parent describes the discomfort of 'having to push the kids out of my home workspace. Having them at home isn't very conducive to home-working and it's not enjoyable for the kids either.' Many expressed frustration trying to turn their homes into full-time childcare centers. As another respondent put it, 'still some tensions with the children who think they're at day camp here at home.'<fnref n="19" /> Others worried about the future for their children,

especially when they would be able to return to school. At the same time, 'children' and 'family' also brought joy during confinement, whether it was reminding people of the simple things in life like playing hide-and-seek, or being able to spend more time with them than usual.

Aside from children, a frequently mentioned topic was going out (sortir). Given the restriction of having to carry the required self-authorization form and only being recommended to go out for a short period of time, respondents often talked about both the frustration and stress associated with outings. In the same vein, going out 'shopping' (for food or other necessities) also elicited quite a few 'bad moments.' Some reported so much fear that they were not able to bring themselves to leave home. One respondent wrote 'no more bread, saving my milk, empty fridge and no one to do my grocery shopping... feeling of total isolation and disinterestedness from the community.'<fnref n="20" /> A third and related key topic about going out revolved around masks, including judging others for not wearing them, the challenges of finding them early on, trying to make them at home, and the difficulty of wearing them. The feelings that respondents were most likely to associate with a moment of difficulty were both anxiety and fatigue.

At the same time, being outside was also a major source of joy that people cherished during lockdown. Yet respondents differentiated this type of outside time from the more stressful moments, especially among those who had easy access to green spaces around their home. Respondents most often mentioned terms like garden/backyard (jardin), walk/stroll (promenade), or simply sun (soleil), all usually within sentences expressing happiness and being content. One panelist commented: 'Taking a walk around the garden. The flowers and plants are growing and it smells nice. A big bowl of fresh air.' <fnref n="21" />

Overall, respondents often waxed philosophical over the meaning of their lives, their relationships, the lockdown, and the pan-

<footnote n="18">18. In a separate model (not shown), we also found a significant effect on well-being from going out, much like we found for stress (see Section 4.2). However, we cannot rule out reverse causality—that is, people feeling subjectively better were more inclined to go out.</footnote>

<footnote n="19">19. 'Avoir à refouler les enfants de mon espace de télétravail. Le contexte avec eux a la maison ne permet pas de télétravailler et ce n'est pas drôle pour les enfants non-plus [sic];' 'Encore des tensions avec les</footnote>

<footnote>enfants qui se croient en centre de loisirs à la maison.'</footnote>

<footnote n="20">20. 'Plus de pain, économie de mon lait, frigo vide et personne pour faire mes courses... sentiment d'isolement total et de désintéressement de la communauté.'</footnote>

<footnote n="21">21. 'Faire un tour de jardin. La végétation pousse et cela sent bon. Un grand bol d'air frais.'</footnote>
demic. And a common theme tying these self-analyses together was how people talked about 'time' in the midst of no longer having a regular schedule as before, pondering and evaluating this change. Many asked themselves about the meaning of their actions during lockdown.

## 4.5. Conclusion

In France, like in most countries worldwide, the COVID-19 pandemic disrupted many people's health, social life, and established habits. Apart from the direct impact of the virus (about 9 percent of the population thought they were infected by May, 2020), what was the impact on morale? Our panel allows us to address the question with a short answer: the large majority did not panic. On the contrary, French residents declared feeling subjectively better than beforehand, and progressively more so after the start of the lockdown. However, our panel also reveals inequalities that deviate from this general trend. Generally speaking, subjective well-being has remained lower for social groups that were already lagging behind in these measures, including women, the most financially vulnerable, people living alone, and the pre-COVID-19 unemployed. The pandemic and the resulting lockdown thus do not appear as game changers in subjective well-being and health reports but rather as amplifiers of pre-existing disparities within society.

# Chapter 5. Framing: A Health or Economic Crisis?

## 5.1. Introduction: Competitive narratives of the crisis

COVID-19 started as a public health concern, with a response geared nearly exclusively at minimizing public health damage (Ferguson et al. 2020) and reversing key policy directions that had included limiting public deficits and curbing hospital funding. In his TV speech on March 12, 2020 the French President, Emmanuel Macron, showed his determination to take all necessary measures to prevent the spread of the virus 'no matter the costs,' a phrase repeated three times.<fnref n="22" /> In a period of public health emergency and with the focus on shutting down or limiting a wide range of economic activities in order to reduce infection rates, the risk of catastrophic short-term economic damage

was downplayed in order to reinforce these efforts. Still, these economic risks were present from the start, right alongside the seemingly more pressing public health risks.

We therefore delved into the concerns about both public health and the economy during lockdown and after in order to determine how people view this tradeoff with two distinct measures. First, we posed a direct question on whether people are more concerned about the impact of the COVID-19 crisis on public health or the economy and devised a barometer repeating the inquiry over all five survey waves (three times during the lockdown and twice when the lockdown was partially and then largely lifted). Second, we explored the volatility of opinions on the tradeoff in 'concern for health' vs. 'concern for the economy' via an indirect experimental question proposed during wave 2.

Our analysis leads to two main findings: 1. concern over the economic impact has been steadily growing in comparison to concern for health between April and June 2020, following the dropping infection rate, and 2. public opinion about reopening was indeed highly volatile and even manipulable in April when the epidemic was at its peak.

We explore each of these findings in the following two sections before concluding.

## 5.2. Trading off between health and the economy

We built our barometer asking panelists if they were more concerned by the health or economic dimension of the COVID-19 crisis. To respond, they were presented with a scale from 0 (concerned entirely with health) to 10 (concerned entirely with the economy) on which they had to position themselves. Overall, concern moved steadily from health to economics. Averaging out all the answers from the beginning of April on this ten-point scale, we found that concern was rather balanced but mildly turned towards health (with an average score of 4.66). Over the following month spent in lockdown, concern began moving back towards economics, reaching a substantial equilibrium between the two matters with average scores of 4.98 (wave 2) and 5.00 (wave 3). Since the end of lockdown in mid-May, the pendulum has continued to swing towards the economy (average score 5.12) with this trend picking up pace with the more complete reopening at the end of May/beginning of June, with

<footnote n="22">22. Macron, E. 2020. Speech from the Elysée on March 12, 2020. https://www.vie-publique.fr/discours/273869-emma-nuel-macron-12032020-coronavirus.</footnote>
the average score sitting at 5.56. This demonstrates that the main concern decisively changed field, with economic fears overtaking health (the robustness of this increased concern for the economy is confirmed when controlling for the main socio-economic covariates, see Table 5.2).

We observe this movement towards growing economic concern in more detail in Table 5.1, which groups respondents into those with a greater concern for health (with a score ranging from 0 to 4), those with an equal worry for both (with a score 5), and those with a greater concern for the economy (with a score ranging from 6 to 10). While the share of those mostly concerned with health accounted for nearly 40 percent of our sample at the beginning of April, this percentage progressively declined in the following four waves, reaching its lowest level at the end of May/beginning of June (23 percent). At the opposite end, the percentage of those more concerned with the economy grew substantially from 32 percent at the beginning of lockdown to 43 percent when lockdown rules had been largely lifted. Interestingly, the percentage of those displaying an equal concern for health and the economy also increased—albeit at a lower rate than the concern for the economy—from 28 to almost 34 percent.

We investigate the socioeconomic factors most strongly associated with these varying concerns in two different sets of regression models. The first one regresses our usual predictors on the self-positioning of the respondent on the 0 (maximum concern for health) to 10 (maximum concern for the economy) scale (Table 5.2). The second associates the same factors with the probability of providing an 'extreme answer.' To capture extreme answers we created a dummy assigning the value 1 to an extreme concern for health or economics (indicated by the answers 0, 1

and 9, 10 on the scale) and 0 to the other more moderate answers (Table 5.3). This model permits us to test whether specific socioeconomic characteristics change not only the sheer concern for health or the economy but also how individuals lived their experience and formulated their opinion in a highly volatile time, assuming more moderate or radical opinions about the main risks facing the country.

Both sets of models do not display many significant associations, which could indicate that concerns for health and the economy are randomly distributed across the population rather than clearly polarized across classic socioeconomic cleavages. However, the appearance of certain associations in individual waves of our survey seems to provide some indication of how different socioeconomic factors might have played a role at only one specific point of the pandemic's progression.

Our regression on the eleven (0-10) point-scale seems to suggest that women are more concerned with health (Galasso et al. 2020), and men are more concerned with the economy. However, these gender differences are significant only after the end of lockdown in mid-May (as shown by the regression models run for data in wave 4 and 5), as we did not detect them in the pooled model. Those with higher levels of education—especially when compared to people with more basic qualifications—displayed a stronger concern for the economy from mid-April onwards. Perhaps unsurprisingly, retirees—as a reflection of their higher risk of severe health complications from contracting the virus—are more concerned with health than those who are inactive or unemployed before lockdown. Unemployed people, given their precarious position in the labor market, seem to be the most concerned with the state of the economy. Top income earners—when compared with those in the bottom quartile of

<table><caption>Table 5.1. Concern for Health vs. Concern for the Economy: Descriptive results</caption><thead><tr><th></th><th>April 1-8</th><th>April 15-22</th><th>April 29-May 6</th><th>May 13-20</th><th>May 27-June 4</th></tr></thead><tbody><tr><th>0-4 (Health)</th><td>39.4%</td><td>36.4%</td><td>31.4%</td><td>29.0%</td><td>23.1%</td></tr><tr><th>5</th><td>28.6%</td><td>28.8%</td><td>34.8%</td><td>34.2%</td><td>33.8%</td></tr><tr><th>6-10 (Economics)</th><td>32.1%</td><td>34.9%</td><td>33.8%</td><td>36.8%</td><td>43.1%</td></tr></tbody></table>
<table><caption>Table 5.2. OLS regressions of concern for the economy vs health on selected socio-demographic characteristics</caption><thead><tr><th></th><th>Pooled vce<br>cluster</th><th>Apr 1-8<br>2020</th><th>Apr 15-22<br>2020</th><th>Apr 29-May 6<br>2020</th><th>May 13-20<br>2020</th><th>May 27-June 4<br>2020</th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>0.191<br>(0.284)</td><td>-0.008<br>(0.354)</td><td>-0.067<br>(0.349)</td><td>0.243<br>(0.338)</td><td>0.214<br>(0.329)</td><td>0.587*<br>(0.321)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>0.315<br>(0.432)</td><td>0.001<br>(0.527)</td><td>-0.205<br>(0.519)</td><td>0.532<br>(0.501)</td><td>0.667<br>(0.495)</td><td>0.648<br>(0.477)</td></tr><tr><td>Woman</td><td>-0.271<br>(0.175)</td><td>-0.109<br>(0.221)</td><td>-0.157<br>(0.218)</td><td>-0.228<br>(0.210)</td><td>-0.435**<br>(0.206)</td><td>-0.431**<br>(0.200)</td></tr><tr><td>Foreign born</td><td>-0.250<br>(0.300)</td><td>-0.080<br>(0.399)</td><td>-0.230<br>(0.393)</td><td>-0.527<br>(0.380)</td><td>-0.266<br>(0.370)</td><td>-0.173<br>(0.361)</td></tr><tr><td>Living alone</td><td>-0.046<br>(0.270)</td><td>-0.051<br>(0.337)</td><td>-0.263<br>(0.334)</td><td>-0.096<br>(0.320)</td><td>0.175<br>(0.313)</td><td>0.012<br>(0.305)</td></tr><tr><td>Living with kids</td><td>-0.104<br>(0.222)</td><td>-0.252<br>(0.276)</td><td>-0.006<br>(0.272)</td><td>-0.211<br>(0.262)</td><td>-0.088<br>(0.259)</td><td>0.039<br>(0.250)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.693***<br>(0.264)</td><td>0.183<br>(0.333)</td><td>0.756**<br>(0.329)</td><td>0.874***<br>(0.317)</td><td>0.854***<br>(0.306)</td><td>0.796***<br>(0.302)</td></tr><tr><td>Some College (Ref=below High school)</td><td>0.658**<br>(0.274)</td><td>0.240<br>(0.320)</td><td>0.733**<br>(0.315)</td><td>0.721**<br>(0.304)</td><td>0.977***<br>(0.298)</td><td>0.645**<br>(0.290)</td></tr><tr><td>Graduate education (Ref=below High school)</td><td>0.993***<br>(0.275)</td><td>0.716**<br>(0.364)</td><td>0.789**<br>(0.359)</td><td>1.243***<br>(0.345)</td><td>1.217***<br>(0.337)</td><td>1.020***<br>(0.330)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>-0.936*<br>(0.518)</td><td>-1.426**<br>(0.597)</td><td>-0.836<br>(0.588)</td><td>-0.747<br>(0.564)</td><td>-0.513<br>(0.551)</td><td>-1.109**<br>(0.541)</td></tr><tr><td>Working (Ref=Retired)</td><td>-0.402<br>(0.356)</td><td>-0.710<br>(0.449)</td><td>-0.720<br>(0.443)</td><td>-0.190<br>(0.426)</td><td>0.128<br>(0.423)</td><td>-0.453<br>(0.407)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>-1.130*<br>(0.609)</td><td>-0.535<br>(0.692)</td><td>-1.756**<br>(0.682)</td><td>-1.388**<br>(0.651)</td><td>-0.846<br>(0.647)</td><td>-1.068*<br>(0.627)</td></tr><tr><td>Household income €2000-€2999<br>(Ref=below €2000)</td><td>-0.054<br>(0.286)</td><td>-0.668**<br>(0.337)</td><td>0.004<br>(0.333)</td><td>0.094<br>(0.322)</td><td>0.275<br>(0.312)</td><td>0.052<br>(0.306)</td></tr><tr><td>Household income € 3000-v3999<br>(Ref=below €2000)</td><td>0.069<br>(0.320)</td><td>-0.396<br>(0.375)</td><td>-0.120<br>(0.370)</td><td>0.241<br>(0.356)</td><td>0.525<br>(0.345)</td><td>0.125<br>(0.340)</td></tr><tr><td>Household income €4000 and more<br>(Ref=below €2000)</td><td>0.407<br>(0.335)</td><td>0.188<br>(0.396)</td><td>0.136<br>(0.391)</td><td>0.527<br>(0.377)</td><td>0.708*<br>(0.370)</td><td>0.499<br>(0.358)</td></tr><tr><td>Financially vulnerable</td><td>0.248<br>(0.389)</td><td>0.309<br>(0.436)</td><td>0.460<br>(0.429)</td><td>0.225<br>(0.424)</td><td>0.323<br>(0.423)</td><td>-0.090<br>(0.395)</td></tr><tr><td>Manager/Professional</td><td>-0.233<br>(0.226)</td><td>-0.293<br>(0.303)</td><td>-0.032<br>(0.299)</td><td>-0.466<br>(0.288)</td><td>-0.311<br>(0.281)</td><td>-0.078<br>(0.275)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.166<br>(0.276)</td><td>0.230<br>(0.375)</td><td>-0.309<br>(0.371)</td><td>-0.236<br>(0.356)</td><td>-0.228<br>(0.347)</td><td>-0.272<br>(0.340)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>-0.134<br>(0.261)</td><td>0.290<br>(0.358)</td><td>-0.408<br>(0.354)</td><td>-0.339<br>(0.339)</td><td>-0.135<br>(0.328)</td><td>-0.071<br>(0.325)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.020<br>(0.297)</td><td>0.402<br>(0.382)</td><td>-0.366<br>(0.378)</td><td>-0.126<br>(0.362)</td><td>-0.114<br>(0.352)</td><td>0.108<br>(0.346)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>0.149<br>(0.265)</td><td>0.344<br>(0.345)</td><td>-0.084<br>(0.340)</td><td>0.208<br>(0.334)</td><td>0.170<br>(0.317)</td><td>0.102<br>(0.312)</td></tr><tr><td>Leftist political orientation (Ref=center)</td><td>-0.374*<br>(0.224)</td><td>-0.718**<br>(0.296)</td><td>-0.459<br>(0.292)</td><td>-0.237<br>(0.281)</td><td>-0.109<br>(0.275)</td><td>-0.328<br>(0.268)</td></tr><tr><td>Rightist political orientation (Ref=center)</td><td>0.418*<br>(0.216)</td><td>0.147<br>(0.287)</td><td>0.339<br>(0.283)</td><td>0.465*<br>(0.272)</td><td>0.643**<br>(0.266)</td><td>0.509*<br>(0.260)</td></tr></tbody></table>
<table><tr><td>coco2 (Apr 15-22 2020)</td><td>0.341***<br>(0.097)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>coco3 (Apr 29-May 6 2020)</td><td>0.438***<br>(0.103)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>coco4 (May 13-20 2020)</td><td>0.489***<br>(0.102)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>coco5 (May 27-June 4 2020)</td><td>0.956***<br>(0.100)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Constant</td><td>4.314***<br>(0.609)</td><td>5.123***<br>(0.764)</td><td>5.485***<br>(0.754)</td><td>4.477***<br>(0.730)</td><td>3.801***<br>(0.714)</td><td>4.796***<br>(0.692)</td></tr><tr><td>Observations</td><td>3,102</td><td>633</td><td>631</td><td>615</td><td>590</td><td>633</td></tr></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

the income distribution—are more concerned with the economy but only after the end of the lockdown (wave 4). Moreover, income, like gender, does not seem to significantly impact the tradeoff in concern for health versus concern for the economy within the pooled model.

Political positioning seems to have a slightly more important effect than income in predicting people's concern for health or for the economy. Those who self-identify on the left of the spectrum seem more concerned with health, and those on the right more so with the economy. However, the effect of the left-wing positioning is significant only in the first survey wave during lockdown, while that of right-wing positioning is more consistent, as we can see across three different survey waves. In the next section we will see how our experimental question in wave 2 allows us to gather additional information on the relation between politics and concerns for health and the economy.

When looking at moderate and extreme opinions, our pooled regression model unveils some other interesting patterns. Education seems to be the key variable in detecting 'extremism' on the issue. Those with the lowest level of education have more moderate opinions than others, possibly due to a lack of reliable information causing this group to be more uncertain and therefore more prudent when asked to take a firm stance on the tradeoff. Two other associations—although weaker and less significant than those detected for education—are found among those who are foreign born and those who position themselves on the right of the political spectrum. Net of all other predictors, foreign born respondents have a significantly more moderate

view than the rest of our sample, while right wingers are significantly more extreme in their opinions about our tradeoff and prevailingly concerned with the economy, as mentioned earlier.

### 5.3. Manipulating opinions on the public health vs. economy tradeoff

Economics studies have shown that the unknown risks that the virus poses preclude utility maximizing behavior in individuals (Glover, Heathcote, Krueger & Rios-Rull 2020). Rather, people interpret the information available to them in order to make a decision about when to go out and work, and when to stay home, stop working, and reduce consumption (Aum, Lee & Shin 2020). The extent of each individual's economic participation is heavily influenced by socio-demographic factors such as age (and pre-existing health concerns), occupation type, and gender (Galasso et al. 2020), leading individuals to try to maximize their income and minimize their inferred health damage that infection would cause.

These individual decisions lead to macroeconomic inefficiencies (Eichenbaum, Rebelo & Trabandt 2020) due to collective uncertainty and subsequent best guesses at how much risk to one's health is tolerable. Additionally, this suggests that any social distancing policy route that is chosen will fail to meet a large swath of individuals' economic participation preferences due to wide variations in perceived risk of infection from one person to the next. Evidence of these wide variations in preferences can also be found in the French population, where those reported to be very worried about their health were significantly more in favor of an extension of strict lockdown rules beyond May 11 (Lambert et al. 2020d).
<table><caption>Table 5.3. OLS regressions of extreme positioning on the 'economy vs health' tradeoff on selected socio-demographic characteristics</caption><thead><tr><th></th><th>Pooled vce<br>cluster</th><th>Apr 1-8<br>2020</th><th>Apr 15-22<br>2020</th><th>Apr 29-May 6<br>2020</th><th>May 13-20<br>2020</th><th>May 27-June 4<br>2020</th></tr></thead><tbody><tr><td>Age 40-60 (Ref=below 40 years)</td><td>0.199<br>(0.371)</td><td>0.392<br>(0.583)</td><td>0.031<br>(0.397)</td><td>0.503<br>(0.588)</td><td>0.363<br>(0.548)</td><td>-0.037<br>(0.439)</td></tr><tr><td>Age above 60 (Ref=below 40 years)</td><td>0.659<br>(0.478)</td><td>0.793<br>(0.773)</td><td>0.407<br>(0.573)</td><td>1.051<br>(0.774)</td><td>0.902<br>(0.770)</td><td>0.562<br>(0.612)</td></tr><tr><td>Woman</td><td>-0.277<br>(0.220)</td><td>0.068<br>(0.321)</td><td>0.047<br>(0.257)</td><td>-0.748**<br>(0.302)</td><td>-0.351<br>(0.343)</td><td>-0.426<br>(0.266)</td></tr><tr><td>Foreign born</td><td>-0.724*<br>(0.388)</td><td>0.108<br>(0.567)</td><td>-1.027<br>(0.641)</td><td>-1.228<br>(0.762)</td><td>-1.526<br>(1.046)</td><td>-0.594<br>(0.562)</td></tr><tr><td>Living alone</td><td>-0.422<br>(0.296)</td><td>-0.148<br>(0.490)</td><td>0.100<br>(0.382)</td><td>-0.527<br>(0.485)</td><td>-1.385**<br>(0.691)</td><td>-0.750*<br>(0.438)</td></tr><tr><td>Living with kids</td><td>-0.155<br>(0.265)</td><td>-0.262<br>(0.404)</td><td>0.096<br>(0.315)</td><td>-0.218<br>(0.385)</td><td>-0.421<br>(0.405)</td><td>-0.222<br>(0.329)</td></tr><tr><td>High school (Ref=below High school)</td><td>0.792***<br>(0.303)</td><td>0.469<br>(0.438)</td><td>0.499<br>(0.363)</td><td>1.070**<br>(0.430)</td><td>1.514***<br>(0.519)</td><td>0.819**<br>(0.395)</td></tr><tr><td>Some College (Ref=below High school)</td><td>0.711**<br>(0.310)</td><td>0.301<br>(0.439)</td><td>0.209<br>(0.368)</td><td>1.140***<br>(0.428)</td><td>1.606***<br>(0.518)</td><td>0.798**<br>(0.391)</td></tr><tr><td>Graduate/post-graduate education (Ref=below High school)</td><td>0.608**<br>(0.305)</td><td>-0.183<br>(0.550)</td><td>0.354<br>(0.415)</td><td>1.153**<br>(0.507)</td><td>1.023*<br>(0.618)</td><td>0.804*<br>(0.450)</td></tr><tr><td>Inactive/School (Ref=Retired)</td><td>0.501<br>(0.489)</td><td>0.414<br>(0.779)</td><td>0.805<br>(0.625)</td><td>0.487<br>(0.743)</td><td>0.737<br>(0.905)</td><td>0.175<br>(0.724)</td></tr><tr><td>Working (Ref=Retired)</td><td>0.233<br>(0.349)</td><td>0.055<br>(0.604)</td><td>0.482<br>(0.487)</td><td>-0.251<br>(0.588)</td><td>0.709<br>(0.659)</td><td>0.317<br>(0.511)</td></tr><tr><td>Unemployed (Ref=Retired)</td><td>0.314<br>(0.671)</td><td>0.419<br>(0.872)</td><td>0.951<br>(0.737)</td><td>-0.715<br>(1.139)</td><td>0.885<br>(0.923)</td><td>0.012<br>(0.847)</td></tr><tr><td>Household income €2000-€2999<br>(Ref=below €2000)</td><td>0.027<br>(0.304)</td><td>-0.211<br>(0.490)</td><td>0.233<br>(0.372)</td><td>0.032<br>(0.469)</td><td>0.153<br>(0.582)</td><td>-0.063<br>(0.413)</td></tr><tr><td>Household income €3000-€3999<br>(Ref=below €2000)</td><td>-0.043<br>(0.352)</td><td>0.133<br>(0.512)</td><td>-0.337<br>(0.431)</td><td>0.088<br>(0.509)</td><td>0.441<br>(0.582)</td><td>-0.356<br>(0.464)</td></tr><tr><td>Household income €4000 and more<br>(Ref=below €2000)</td><td>-0.005<br>(0.357)</td><td>0.104<br>(0.549)</td><td>-0.108<br>(0.445)</td><td>0.081<br>(0.531)</td><td>0.185<br>(0.618)</td><td>-0.159<br>(0.472)</td></tr><tr><td>Financially vulnerable</td><td>0.570<br>(0.386)</td><td>0.200<br>(0.602)</td><td>0.808*<br>(0.438)</td><td>0.702<br>(0.565)</td><td>0.660<br>(0.709)</td><td>0.400<br>(0.542)</td></tr><tr><td>Manager/Professional</td><td>-0.432<br>(0.266)</td><td>-0.190<br>(0.429)</td><td>-0.566<br>(0.365)</td><td>-0.936**<br>(0.415)</td><td>-0.386<br>(0.445)</td><td>-0.104<br>(0.356)</td></tr><tr><td>Urban area 0.2m-2m inhabitants (Ref=Paris)</td><td>-0.189<br>(0.328)</td><td>-0.136<br>(0.569)</td><td>0.074<br>(0.432)</td><td>-0.865*<br>(0.504)</td><td>-0.175<br>(0.573)</td><td>-0.017<br>(0.480)</td></tr><tr><td>Urban area 2000 - 0.2m inhabitants (Ref=Paris)</td><td>-0.079<br>(0.307)</td><td>0.185<br>(0.518)</td><td>-0.371<br>(0.430)</td><td>-0.490<br>(0.453)</td><td>-0.121<br>(0.543)</td><td>0.372<br>(0.454)</td></tr><tr><td>Rural area (Ref=Paris)</td><td>-0.019<br>(0.317)</td><td>0.048<br>(0.561)</td><td>-0.055<br>(0.443)</td><td>-0.421<br>(0.480)</td><td>-0.078<br>(0.572)</td><td>0.367<br>(0.478)</td></tr><tr><td>Flat size less than 25 sqm p.p.</td><td>-0.078<br>(0.363)</td><td>0.265<br>(0.492)</td><td>0.006<br>(0.400)</td><td>-0.118<br>(0.486)</td><td>-0.208<br>(0.525)</td><td>-0.218<br>(0.441)</td></tr><tr><td>Leftist political orientation (Ref=center)</td><td>0.075<br>(0.282)</td><td>0.336<br>(0.474)</td><td>0.768*<br>(0.406)</td><td>-0.075<br>(0.415)</td><td>-0.084<br>(0.542)</td><td>-0.562<br>(0.380)</td></tr><tr><td>Rightist political orientation (Ref=center)</td><td>0.517*<br>(0.269)</td><td>0.645<br>(0.454)</td><td>1.029***<br>(0.393)</td><td>0.105<br>(0.395)</td><td>0.842*<br>(0.486)</td><td>0.166<br>(0.337)</td></tr></tbody></table>
<table><tr><td>coco2 (Apr 15-22 2020)</td><td>0.939***<br>(0.161)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>coco3 (Apr 29-May 6 2020)</td><td>0.245<br>(0.161)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>coco4 (May 13-20 2020)</td><td>0.010<br>(0.166)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>coco5 (May 27-June 4 2020)</td><td>0.473***<br>(0.162)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Constant</td><td>-3.277***<br>(0.720)</td><td>-3.624***<br>(1.121)</td><td>-2.856***<br>(0.915)</td><td>-2.384**<br>(1.081)</td><td>-4.318***<br>(1.240)</td><td>-2.507***<br>(0.935)</td></tr><tr><td>Observations</td><td>2,971</td><td>633</td><td>500</td><td>615</td><td>590</td><td>633</td></tr></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

To delve further into the inherent tradeoff between the health and the economic dimensions of the pandemic, we administered an experiment during the second wave of our survey (April 15-22). Panelists were randomly divided into two groups and asked to consider the issue of a partial reopening of the French economy starting on May 11, 2020, as intended by the government. The first group was provided a scenario in which the number of infected people had not diminished as much as foreseen and asked if reopening on May 11 should still occur, or rather if strict lockdown measures should be extended. The second group was provided with the same scenario but was also told that 'experts fear that extending the lockdown could further aggravate the economic crisis, leading to millions of unemployed and the bankruptcy of up to 25% of all companies.'

Despite the fact that this tradeoff has been a recurrent topic in the news, the result of our experiment is striking. Support for reopening the economy on May 11, regardless of the number of active cases, stood at 36 percent in the first group, but jumped to 66 percent for those in the second group who had been warned of the damage that a protracted lockdown could do to the economy. Our findings suggest that collective views on the matter were highly susceptible to manipulation: a simple treatment (i.e. two lines about a pessimistic economic scenario) can shift opinions about key policy measures, such as keeping an entire nation under strict lockdown rules. In a climate of uncertainty and confusion in which contradictory information is widespread and governmental decisions have been adjusted on an ad hoc basis, our experiment demonstrates that receiving information from a seemingly authoritative source can easily sway public opinion (Barnes & Hicks 2018).

We further analyzed the results of this experiment across several covariates (Table 5.4). In the untreated group only 30 percent of women and 42 percent of men supported reopening. However, women tended to react more strongly than men to the prospect of a potential economic crisis. Presenting additional information on the economic consequences of a continued lockdown heightens the support for ending those strict measures, especially among women. This translates into 67 and 66 percent of men and women respectively in the treated group who support reopening.

A substantial convergence between the first and the second group can also be found across age bands. Echoing the findings we previously presented about the strong concern that retired people display for the economy, those over the age of 60 were more influenced by our treatment of a potentially catastrophic economic scenario: 71 percent of them preferred to reopen rapidly, while the other age groups displayed a score below the overall average of the sample after treatment. This phenomenon is confirmed in other studies on age differences in opinions of lockdown extension prior to May 11 (Lambert et al. 2020d).

The more highly educated were strongly in favor of reopening when faced with the high unemployment scenario. While high-earners and low-earners were similarly likely to support the reopening (39 and 34 percent, respectively), support among high earners was significantly stronger when they received more information about the potential economic crisis (75 and 59 percent, respectively). Also, people self-positioning themselves at the top of the social ladder (as indicated by their self-classification on scale from 1-10, where 1 means 'ranking at the bottom
of society,' 10 means 'ranking at the top of society') were very much in favor of reopening quickly, 42 percent in the untreated group and 80 percent (the highest value we recorded among any social group) in the treated group.

Other interesting findings can be seen in variables strictly related to COVID-19. Possibly because they consider themselves immune, those who had reported being infected were much more likely to support reopening than those who were not in

the untreated group (35 vs 48 percent), with this considerable difference then diminishing after treatment. Perhaps unsurprisingly, those with higher levels of lockdown-related stress were much more likely to be in favor of reopening (in both groups) with scores above the sample average.

Finally, the results related to political cleavages present an interesting pattern, characterized by divergence among the untreated group and strong convergence after treatment. Those

<table><caption>Table 5.4. Support for End of Lockdown: Comparison across Experimental Groups</caption><thead><tr><th></th><th>N</th><th>Mean Group A</th><th>Mean Group B</th><th>Effect Treatment</th><th>P Value</th></tr></thead><tbody><tr><td>All</td><td>984</td><td>36.0</td><td>66.0</td><td>30.0</td><td>0</td></tr><tr><td><strong>Gender</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Men</td><td>442</td><td>42.4</td><td>66.7</td><td>24.3</td><td>0</td></tr><tr><td>Women</td><td>505</td><td>30.1</td><td>66.1</td><td>36.0</td><td>0</td></tr><tr><td><strong>Age</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>below 40</td><td>119</td><td>40.3</td><td>63.5</td><td>23.2</td><td>0</td></tr><tr><td>40 to 60</td><td>423</td><td>31.1</td><td>63.7</td><td>32.6</td><td>0</td></tr><tr><td>above 60</td><td>405</td><td>37.8</td><td>71.1</td><td>33.3</td><td>0</td></tr><tr><td><strong>Education</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>below High school</td><td>337</td><td>30.0</td><td>63.0</td><td>33.0</td><td>0</td></tr><tr><td>High school</td><td>180</td><td>48.6</td><td>59.2</td><td>10.7</td><td>0.173</td></tr><tr><td>Some College/College Graduate</td><td>194</td><td>32.0</td><td>70.0</td><td>38.0</td><td>0</td></tr><tr><td>Graduate degree</td><td>236</td><td>42.8</td><td>75.2</td><td>32.4</td><td>0</td></tr><tr><td><strong>Household income</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lowest (less than €2000)</td><td>204</td><td>34.2</td><td>59.0</td><td>24.8</td><td>0</td></tr><tr><td>Lower middle (€2000-€2999)</td><td>243</td><td>36.7</td><td>60.6</td><td>23.9</td><td>0</td></tr><tr><td>Upper middle (€3000-€3999)</td><td>167</td><td>34.0</td><td>61.2</td><td>27.2</td><td>0</td></tr><tr><td>Highest (€4000 and more)</td><td>236</td><td>39.0</td><td>75.2</td><td>36.2</td><td>0</td></tr><tr><td><strong>Subjective Social Position</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Scores 1-4</td><td>292</td><td>37.8</td><td>61.4</td><td>23.6</td><td>0</td></tr><tr><td>Scores 5-6</td><td>352</td><td>32.2</td><td>63.0</td><td>30.8</td><td>0</td></tr><tr><td>Scores 7-10</td><td>164</td><td>42.2</td><td>80.1</td><td>37.8</td><td>0</td></tr><tr><td><strong>Covid</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No Covid</td><td>894</td><td>34.6</td><td>65.7</td><td>31.1</td><td>0</td></tr><tr><td>Had Covid</td><td>90</td><td>47.7</td><td>69.5</td><td>21.8</td><td>0.03</td></tr><tr><td>Lockdown-related stress</td><td>354</td><td>30.3</td><td>60.4</td><td>30.1</td><td>0</td></tr><tr><td>Scores 1-4</td><td>260</td><td>36.2</td><td>63.7</td><td>27.6</td><td>0</td></tr><tr><td>Scores 5-6</td><td>370</td><td>40.1</td><td>73.7</td><td>33.6</td><td>0</td></tr><tr><td>Scores 7-10</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Political Preferences</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Right</td><td>392</td><td>43.6</td><td>66.6</td><td>22.9</td><td>0</td></tr><tr><td>Centre</td><td>214</td><td>28.3</td><td>64.6</td><td>36.3</td><td>0</td></tr><tr><td>Left</td><td>337</td><td>34.0</td><td>66.3</td><td>32.3</td><td>0</td></tr></tbody></table>

Note: Mean Group A indicates the level of support for end of lockdown when only information about health is provided. Mean Group B indicates the level of support for end of lockdown when the treatment effect of additional information on economic losses is provided.
on the right were largely supportive of reopening (44 percent), followed by those on the left (34 percent), and those who positioned themselves at the center (28 percent). In the treated group, we find a substantial realignment (67 percent among both right- and left-wingers, 65 percent of those in the center).

### 5.4. Conclusion

During moments of crisis and when uncertainty prevails, the simplicity and believability of ideas win out over their validity (Stanley 2014). This has been demonstrated in several political economics studies of austerity policies following economic downturns where the narrative of debt becomes one of a moral obligation and less an economic fact, and where microeconomic household budget balancing is wrongly correlated with the macroeconomic functioning of the state (Blyth 2013; Krugman 2012).

These narratives, around which interpretations of crises and appropriate responses are built, spread in a multitude of ways. In another historical instance, the media helped set the tone of crisis during the political transition moment of the Winter of Discontent in the UK in the late 1970s (Hay 1996). Specifically, newspapers created an atmosphere of urgency and believability by interpellation of the reader, discursive selectivity of content, and meta narratives of crisis that overarched many headlines and stories. What becomes obvious then is how narratives do not necessarily stem from elites' preferences, but rather from the intersubjective contexts of many agents, both elite and everyday (Seabrooke 2007), affecting individuals across socioeconomic factors. In other words, social shifts driven by a compounding of individual preferences drive institutional change and vice versa, especially in times of crisis (Widmaier, Blyth & Seabrooke 2007).

As for the malleability of public opinion that we see with lockdown in France, this moment of exogenous shock is providing a window of opportunity for a new 'mood of the day' (Stanley 2014). Alternative narratives to interpret the chosen policy route on the economic vs. health tradeoff will arise from a combination of authoritative recommendations and everyday social interactions. While economic models show that no single approach or policy implementation can possibly be demonstrated as the best way forward, cohesion has and will continue to form

around the simplest and most believable ideas given the information at hand. We demonstrated that this available information, when presented to individuals with highly elastic opinions, is a more powerful indicator of policy preferences than nearly all socioeconomic factors we considered. Even traditional political cleavages seem to disappear in the face of a strong and authoritative message.

In sum, regardless of individual characteristics, having additional information on the potential economic damage from protracted lockdown seems to have had a strong effect on people's support for reopening the country as planned by the French government on May 11. Providing information about potentially catastrophic economic outcomes substantially reshaped people's concerns, moving the focus from health to the economy. This finding has to be contextualized in an environment where overall concern for the economy was in the process of winning ground over concern for public health as France exited lockdown. New events and epidemic waves may reverse this trend in the future.
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Abstract

This working paper offers an overview of the first stage of the Coping with Covid (CoCo) project, which tracks the beha-
viors and attitudes of a representative panel of the French metropolitan population during the COVID-19 lockdown. We
conducted five survey waves and administered daily journals of open-ended responses between April and June 2020
among a sample of 1,216 people from a pre-existing panel (ELIPSS). Earlier surveys of this sample allowed us to better
contextualize changes that may have occurred during this unusual period.

We outline four experiential dimensions during the lockdown period: relation to work, everyday activities and time use, self-assessed health and well-being, and the framing of the pandemic crisis. What we found follows traditional inequality patterns and also reveals some unexpected changes in social practices and attitudes.

More information related to the CoCo Project: https://www.sciencespo.fr/osc/fr/content/faire-face-au-covid-19.html

Keywords

COVID-19, Lockdown, Social inequality, Gender inequality, Well-being, Health

Version électronique, en ligne / On-line version

http://www.sciencespo.fr/osc/fr/content/osc-papers

Également disponible sur SPIRE, l'archive ouverte de Sciences Po.

https://spire.sciencespo.fr/web/

Pour citer cet article / To cite this article

Ettore Recchi, Emanuele Ferragina, Olivier Godechot, Emily Helmeid, Stefan Pauly, Mirna Safi, Nicolas Sauger, Jen Schradie, Katharina Tittel, Andrew Zola, Living through Lockdown: Social Inequalities and Transformations during the COVID-19 Crisis in France", Sciences Po OSC Papers, n° 2020-1, July 2020.

Edition de ce numéro / Editorial board

Bernard Corminboeuf (valorisation de la recherche, Sciences Po-CNRS).

bernard.corminboeuf@sciencespo.fr

Observatoire Sociologique
du Changement

27 rue Saint-Guillaume

75337 Paris cedex 07

01 45 49 54 50

http://www.sciencespo.fr/osc/fr/

Responsable de la publication :
Mirna Safi

Responsable éditoriale :
Agnès van Zanten

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OSC 2020