Spatial Inequalities during and after the COVID-19 Pandemic, Laura Khoury

OECD Seminar: Spatial Inequalities during
and after the COVID-19 Pandemic
A Poorly Understood Disease? The Impact of COVID-19 on the
Income Gradient in Mortality over the Course of the Pandemic
Paul Brandily (PSE), Clément Brébion (CBS), Simon Briole (JPAL-PSE),
Laura Khoury (NHH)
July 8th
, 2021

Introduction
I An unprecedented worldwide decline in mortality over the last
century but still a substantial income gradient within most countries
(Case et al., 2002; Cutler et al., 2006; Currie et al., 2020)
→ Chetty et al. (2016): since 2000 in the US, +3 years in the life
expectancy of the top 5% vs. 0 for the bottom 5%
I Explained by:
n
Individual factors: information and education to healthy behaviours,
economic and social stress, access to health care, etc.
n
Ecological factors: living environment, presence of health care
infrastructures, density, pollution, etc.
1/32

Introduction
I Pandemic = sudden exogenous shock (i) reveals latent inequalities
and (ii) potentially amplifies them by spreading differently across
living environments (Alfani, 2021; Beach et al., 2021)
n
Only a few studies in the case of pandemics
n
Papers focusing on COVID-19 essentially correlational and cover the
first months of the epidemic (Chen and Krieger, 2021; Abedi et al.,
2020; Ashraf, 2020; Jung et al., 2020; Decoster et al., forthcoming;
Glaeser et al., 2020; Almagro and Orane-Hutchinson, 2020)
What is the impact of COVID-19 on the pre-existing spatial
gradient in mortality and what are the underlying mechanisms?
2/32

Our paper (1/2)
I Analysis of municipality-level variations within urban areas
across the French territory
I Quantification of the COVID-19-specific income gradient by using an
excess mortality measure
→ +30% in excess mortality in the poorest municipalities on average in 2020
→ 1.3 excess mortality-income elasticity
I Persistence of the gradient over both waves: size of the gradient
increases with the size of the mortality shock (at the urban-area
level)
→ suggests that the “harvesting effect” cannot explain the whole gradient
3/32

Our paper (2/2)
I Distinction between the epidemic-induced vs. lockdown-induced
effect on the gradient
→ no independent contribution of the lockdown
I Exploration of housing condition and occupational exposure
mechanisms
→ Capture a substantial share of the gradient
4/32

Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
5/32

Definitions - Data
I Definitions :
n
Unit of analysis = municipality within the 621 French urban
areas. 16,640 municipalities. Median ≈ 784 inhab., 11km2
n
Urban areas = poles gathering at least 1,500 jobs and their periphery
based on commuting patterns ≈ local labor markets. Account for
about 85% of the population.
n
Départements: administrative units usually bigger than urban areas
I Data :
n
Death registry provided by the National Statistical Institute (INSEE)
at the individual level: information on date of death (day),
municipality of residency, place of death (hospital, home, elderly
care home), gender, age
n
Matched with information on the labour market, housing conditions,
municipalities’ demographics and income from administrative payroll
data, survey, Census.
6/32

Measures - All-cause excess mortality
Dp
m =
Np,2020
m − [0.5 × (Np,2018
m + Np,2019
m )]
Population2014
m
(1)
Np,y
m : number of deaths of residents of municipality m during period p of
year y
Populationm,2014: total number of inhabitants (/10,000) of municipality
m, as recorded in 2014, the most recent available year in our data.
7/32

Measures
All-cause excess mortality avoids numerous biases present in
COVID-19 infection or mortality data:
I Limited testing at the beginning of the epidemic and not randomly
distributed
I All COVID-19 cases or deaths are not always attributed as such
I Indirect deaths
I Excess mortality accounts for the pre-existing gradient
Poverty: bottom 25% of the national weighted distribution of
municipalities’ median standard of living
Robust to alternative definitions
8/32

A municipality-level approach
Municipal vs. Individual data: effect of living in a poor environment vs.
being poor
I Ecological approach
I Pr(DeathCovid ) function of the transmission and lethality
probabilities
I Individuals at risk of transmission not necesarily the ones at risk of
dying
I Important spillovers in case of an epidemic
I Policymakers need to identify clusters
9/32

Table of Contents
5 Mechanisms
6 Conclusion
10/32

Figure 1: Monthly counts of excess deaths in French urban areas
NOTE: The figure represents the difference between the monthly number of deaths in 2020 and its
average over 2019 and 2018 in the relevant zone. The “red” zone corresponds to the areas that
were the most severely hit by the first wave, and that are located in the North-Eastern quarter of the
country. This zone covers about 44% of the urban population of (mainland) France. The “green” zone
encompasses the rest of the French territory.
11/32

Figure 2: Cumulative excess mortality rate per 10,000 inh. by poverty
status
NOTE: The graph plots the cumulative sum of all excess deaths per 10,000 inhabitants from January
2020 for poor and non-poor municipalities. Poor is defined as belonging to the bottom quartile of the
national distribution of municipal median income weighted by the municipality size.
12/32

Table of Contents
5 Mechanisms
6 Conclusion
13/32

Empirical analysis
Dp
[m,ua] = β.Q1[m,ua] + Xp
[m,ua].Λ + γua + νp
[m,ua] (2)
I γua allows us to only exploit differences between municipalities
located in a contiguous urban environment.
I Xp
[m,ua] includes the total population and the share of the population
above 65 years old.
I Standard errors are clustered at the urban-area level.
I Identification assumption: absent COVID-19 and the associated
public policies, the average difference in the evolution of mortality
over period p (2020 vs. before) between rich and poor municipalities
of the same urban area would have remained stable
14/32

Empirical analysis
Figure 3: Monthly difference in all-cause excess mortality by income
15/32

Empirical analysis
Table 1: Excess mortality rate by municipality income
(1) (2) (3) (4)
2020 Wave 1 Wave 2 No wave
Q1 (poor) 2.627*** 1.178* 1.083*** 0.366
(0.996) (0.672) (0.359) (0.297)
Controls X X X X
Urban area FE. X X X X
Non poor average 8.668 3.661 4.825 0.182
Observations 16640 16640 16640 16640
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the urban-area level.
NOTE: This table reports the coefficients associated with equation 2. The independent variable, excess
mortality rate, is computed considering four different time periods: the whole year (column 1), wave 1
(March to April, column 2), wave 2 (October to December, column 3) and other months in 2020 outside
the two waves (January, February and from May to September, column 4). By construction, column 1
is the sum of column 2 to 4. The non-poor average line reports the mean of the dependant variable in
non-poor municipalities. Controls include total population size and the share of the population over 65
years old.
16/32

Robustness checks
I Monotonicity of the gradient Table
I Main specification with deciles or log(income): +1 excess deaths for
a 10% lower income Table
I Falsification test Figure
I Main specification excluding elderly care homes Figure
I Main specification by age group Figure
17/32

Table of Contents
5 Mechanisms
6 Conclusion
18/32

Epidemic and Lockdown-induced effects
I Epidemic-related sources of the income gradient: increase with the
level of infection
n
Related to COVID-19 infections, e.g. reported and unreported
infection-caused deaths
n
Related to COVID-19 spread, e.g. deaths due to altered access to
health services, anxiety due to the level of infection, etc.
I Policy-related sources of the income gradient
n
Related to COVID-19 lockdown, e.g. deaths of despair (Mulligan,
2021), car crashes (Brodeur et al., 2021), domestic violence
(Bullinger et al., 2020), etc.
19/32

Quasi-experiment
I First lockdown (March 17 - May 11) implemented uniformly over the
country while the epidemic was at very heterogeneous stages of
development across départements → triple-difference strategy
I Identification assumption: the evolution of the within urban-area
gradient in the absence of COVID-19 and associated policies would
have been similar on average in red and green zones
I Gradient in green zones = gradient in the independent effect of the
lockdown
I Gradient in red zones - gradient in green zones = net
epidemic-induced gradient
20/32

Identification strategy - Green vs Red
Figure 4: High- (red) and low-infection (green) Départements as of May 7,
2020
21/32

I Pre-lockdown indicators Table
n
Average occupancy rate of intensive care beds on March 18: 26.5%
in red départements; 7.0% in the green départements.
n
Likelihood to visit an emergency unit for suspicion of COVID-19 on
March, 18:12.0% in the red départements; 6.3% in the green
départements.
n
Le Bras (2020) and Fouillet et al. (2020) provide evidence on the
absence of a relationship between the location of the first epicentres
and socio-demographic characteristics at the département level.
22/32

Double-differences by zone
Figure 5: Excess mortality by income and zone
NOTE: The graph plots the β from equation 2 evaluated each month on each zone separately. It
accounts for the monthly difference in all-cause excess mortality between the poor municipalities
and the rest in each zone. The red zone corresponds to the areas that were the most severely hit
by the first wave, and that are located in the North-Eastern quarter of the country. The green
zone encompasses the rest of the French territory. 23/32

The triple-difference setting
Dp
[m,d] = β.Q1m + δ.Redd + ρ.Redd .Q1m + Xp
[m,d].Λ + γua + νp
m (3)
Figure 6: Income gradient in the direct effect of COVID-19 on mortality
NOTE: The graph plots ρ from equation 3 evaluated each month. It accounts for the
monthly difference in all-cause excess mortality between the poor municipalities and the
rest in the red and in green zones.
24/32

Table of Contents
5 Mechanisms
6 Conclusion
25/32

The choice of mechanisms
I Causal impact of COVID-19 on mortality inequalities but income
can cover many potential mechanisms
I Pre-existing conditions play a major role (Wiemers et al., 2020;
Raifman and Raifman, 2020): ignored here because of lack of data
but control for age
I Ecological approach: more interested in mechanisms affecting the
transmission probability
I Significant gradient in incidence rate → the gradient in mortality
cannot be fully explained by differences in lethality (more related to
individual factors) Figure
I Labour market and housing conditions pointed as potential
mechanisms very early on
26/32

Occupational exposure
I Measure of exposure in normal conditions: use of a pre-COVID
survey (DEFIS) that measures the "frequency of direct contact with
the public" for each 3-digit level occupation code
I List of essential workers from the Paris Region Health Observatory:
occupations and sectors which kept on going to their workplace
during lockdown
I Both measure mapped to exhaustive social security records (DADS)
from forms firms are compelled to fill in yearly
n
We observe occupation and sector of employment at a very detailed
level, municipality of work and residency
n
We compute (i) the worker-weighted average frequency of contact;
(ii) the share of essential workers in each French municipality
27/32

Housing conditions
I Share of overcrowded housing units based on Census data (based on
total size of the housing and household, and number of rooms)
I Share of multi-generational households = with at least 1 member
over 65yo and a younger one currently employed (only from Census
files in municipalities with more than 2,000 inhab.)
28/32

Relation with poverty
Table 2: Relationship between poverty status and
mechanism variables
Index of frequent contact Share of essential workers Share of over-crowded housing Multigenerational hh
Main coefficient 0.13598*** 0.15640*** 0.28968*** 0.06955
(0.04143) (0.01664) (0.04138) (0.05136)
Urban areas FE X X X X
Controls X X X X
Control outcome mean 0.251 0.251 0.251 0.272
Observations 16267 16267 16267 3971
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses are clustered at the département level. This table shows
the result of regressing the poverty dummy on mechanism variables measuring either housing conditions or occupational exposure
and the interaction of these variables. The poverty dummy is equal to one for the bottom 25% of the national distribution of
municipal median income weighted by population size. Each column reports the result of a separate regression examining one
mechanism. The main coefficient corresponds to the correlation between each of the mechanism variable and poverty. The
mechanism variables have been standardized such that coefficients can be interpreted in terms of the effect of a one standard-
deviation change, and can be compared with each other. All regressions include urban areas fixed-effects and control for total
population and for the share of inhabitants over 65 y.o. in the municipality.
29/32

Table 3: Horse-race between mechanisms Incidence
(1) (2) (3) (4) (5)
Excess mortality rate, wave 1
Poor 0.51309* 0.40407* 0.33580 0.01338 -0.09418
(0.29809) (0.22907) (0.27580) (0.13489) (0.14011)
Index of frequent contact 0.54991** -0.11774
(0.26948) (0.16746)
Share of essential workers 0.51327*** 0.42277***
(0.10279) (0.12855)
Share of over-crowded housing 1.50339*** 1.45786***
(0.28401) (0.30927)
Urban areas FE X X X X X
Controls X X X X X
Control outcome mean 3.689 3.709 3.687 3.795 3.786
Adjusted R2 0.1710 0.1720 0.1722 0.1762 0.1768
Observations 16267 16267 16267 16267 16267
Excess mortality rate, wave 2
Poor 0.45548*** 0.39177*** 0.17436 0.41355** 0.16927
(0.15906) (0.14304) (0.13468) (0.20919) (0.17754)
Index of frequent contact 0.32137* -0.36327*
(0.16477) (0.21621)
Share of essential workers 0.81386*** 0.98330***
(0.12239) (0.14665)
Share of over-crowded housing 0.12615 0.05592
(0.24226) (0.22109)
Controls X X X X X
Adjusted R2 0.1274 0.1276 0.1298 0.1274 0.1299
Observations 16267 16267 16267 16267 16267
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the
urban-area level. 30/32

Table of Contents
5 Mechanisms
6 Conclusion
31/32

Conclusion
I We provide clear evidence that COVID-19 contributes to increasing
spatial inequalities in mortality through an unequal impact across
municipalities
I The income gradient persists over waves and is the strongest in the
most affected urban areas
I Policy responses to COVID-19 do not play a significant role in the
income gradient
I Suggestive evidence on the mediating role of LM and housing
conditions: ecological factors are key determinants of the spread of
epidemics
I Housing mechanisms are more important during the 1st
wave, LM
mechanisms are more important during the 2nd
wave
32/32

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Chaudhary, Ayesha Khan, Shima Shahjouei, Jiang Li, and Ramin
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Back
(1) (2) (3) (4)
Q1 (poor) 4.216*** 1.986** 1.975*** 0.255
(1.157) (0.796) (0.416) (0.347)
Q2 2.280** 1.170** 1.476*** -0.365
(0.926) (0.484) (0.406) (0.380)
Q3 2.229*** 1.122*** 1.041** 0.0661
(0.585) (0.339) (0.440) (0.328)
Controls X X X X
Q4 average 6.584 2.674 3.682 0.228
Observations 16640 16640 16640 16640
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the
urban-area level.
NOTE: This table reports the coefficients associated with equation ??. The independent
variable, excess mortality rate, is computed considering four different time periods: the
whole year (column 1), wave 1 (March to April, column 2), wave 2 (October to December,
column 3) and other months in 2020 outside the two waves (January, February and from
May to September, column 4). By construction, column 1 is the sum of column 2 to 4.
Controls include total population size and the share of the population over 65 years old.

Back
(1) (2) (3) (4) (5) (6) (7) (8)
2020 Wave 1 Wave 2 No wave 2020 Wave 1 Wave 2 No wave
Q1 (poor) 6.822*** 3.235** 3.109*** 0.478
(1.402) (1.283) (0.557) (0.444)
Q2 5.543*** 2.589*** 2.030*** 0.924
(1.036) (0.444) (0.520) (0.587)
Q3 3.629*** 1.960*** 1.932*** -0.263
(0.913) (0.397) (0.499) (0.551)
Q4 4.343*** 2.080*** 2.155*** 0.108
(1.096) (0.550) (0.516) (0.491)
Q5 3.439*** 1.964*** 1.354** 0.121
(1.119) (0.412) (0.552) (0.603)
Q6 4.052*** 1.994*** 1.714*** 0.344
(0.647) (0.272) (0.497) (0.428)
Q7 3.647*** 1.865*** 1.500** 0.281
(1.010) (0.361) (0.676) (0.565)
Q8 3.277*** 1.795*** 0.677* 0.805
(0.760) (0.350) (0.377) (0.510)
Q9 1.818*** 0.970*** 0.553* 0.294
(0.450) (0.314) (0.331) (0.323)
log Median Income -10.07*** -4.311*** -4.820*** -0.934
(1.993) (1.479) (0.751) (0.766)
Controls X X X X X X X X
Urban area FE. X X X X X X X X
Richest average 5.079 1.945 3.232 -0.0984
Observations 16640 16640 16640 16640 16640 16640 16640 16640

Figure 7: Falsification test Back
Note: This Figure plots the coefficient β as estimated from equation (2) using two alternative depen-
dent variables. The black solid line shows the relative evolution of excess mortality in 2019 for poorest
municipalities (Q1) as compared to others. For the sake of comparison, we also display the excess
mortality of 2020 as compared to the same, 2018 baseline.

Table 6: No elderly care homes Back
(1) (2) (3) (4)
Q1 (poor) 2.524** 1.021 1.139*** 0.363
(1.004) (0.648) (0.310) (0.327)
Controls X X X X
Non poor average 6.486 2.784 3.831 -0.129
Observations 16640 16640 16640 16640
NOTE: This table reports the coefficients associated to equation ??. The independent variable, excess
mortality rate, is computed considering four different time periods: the whole year (column 1), wave 1
(Mars to April, column 2), wave 2 (October to December, column 3) and other months in 2020 outside
the two waves (January, February and from May to September, column 4). By construction, column 1
is the sum of column 2 to 4.

Figure 8: Income gradient (LHS) and Excess mortality rate (RHS) over
2020 by age category Back
NOTE: This Figure plots on the left-hand side the coefficient β as estimated from equation (2)
run separately on excess mortality over 2020 for different age categories. The first point reports the
coefficient estimated on the whole population. 95% confidence intervals are reported. On the right-hand
side, we show the magnitude of excess mortality defined as the number of excess deaths per 10,000
inhabitants over 2020, for the same age categories.

Table 7: Mortality trends in red and green zones Back
(1) (2) (3)
Red zone Green zone Difference
2019 excess mortality rates (2019 vs 2018)
Annual excess mortality rate 0.239 1.473 -1.234
March-April -1.357 -0.685 -0.672
2020 excess mortality rates (2020 vs 2018-2019)
January (pre-COVID) -0.225 -0.457 0.232
February (pre-COVID) -0.355 -0.434 0.080
March 0.816 -0.087 0.904***
- first 2 weeks -0.252 -0.165 -0.088
- last 2 weeks 1.139 0.216 0.923***
April 2.696 0.333 2.364***
May 0.341 0.359 -0.018
Nb municipalities 7,358 9,947 1,7305
Total population 23,457,888 30,460,492 53,918,380
NOTE: Mortality rates are expressed as number of deaths per 10,000 inhabitants.

Table 8: Triple-difference regression on the number of
deaths, wave 1 Back
Number of deaths, wave 1
2020=1 0.12952*** 0.18605***
(0.03773) (0.05675)
2020=1 × Red area=1 0.51121*** 0.75283***
(0.17458) (0.26704)
2020=1 × Poor=1 -0.05089 0.19666*
(0.03267) (0.11114)
2020=1 × Red area=1 × Poor=1 0.81664* 2.41010**
(0.41663) (1.08131)
Municipality FE X X
Sample
All Major urban centres
municipalities and their outskirts
Control outcome mean 1.736 2.290
Observations 188688 100686
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the département
level.
NOTE: This table reports coefficients from a triple-difference regression on the number of deaths in
wave 1 (March-April) over the years 2018-2020. The three dimensions are (i) time, with 2020 being the
post period; (ii) the level of infection in the département before the first wave; (iii) the poverty status
of the municipality, defined as the bottom quartile of the national distribution of municipal income
weighted by the municipal population size. We include municipality fixed-effects to account for time-
invariant factors at the municipal level, such as the population measured in 2014. The control outcome
mean is the mean number of deaths in March-April in the years 2018-2019 in non-poor municipalities
in green zones.

Figure 9: Weekly gradient in incidence rate Back
NOTE: The graph plots the point estimate and the 95% confidence intervals of the estimation of β
from equation 2 evaluated each week with the incidence rate as the dependent variable. It accounts
for the weekly difference in incidence rate between the poor municipalities and the rest, where poor is
defined as belonging to the bottom quartile of the national distribution of municipal median income
weighted by the municipality size.

Table 9: Horse-race between mechanism variables Back
(1) (2) (3) (4) (5)
Incidence rate (per 100k. inh.), wave 2
Poor (Q1) 5.92693*** 5.02211** 4.18254* 0.04053 -1.06828
(2.11590) (2.36330) (2.15177) (2.56955) (2.17981)
Index of frequent contact 4.55486* -3.46067
(2.45458) (3.40661)
Share of essential workers 5.04624* 4.89758
(2.98801) (4.02379)
Share of over-crowded housing 17.69906*** 18.00957***
(1.73201) (2.18879)
Controls X X X X X
Adjusted R2 0.6922 0.6928 0.6934 0.6990 0.6997
Observations 16267 16267 16267 16267 16267
This table shows the result of regressing municipalities’ incidence rate on a variable measuring either
poverty, housing conditions or occupational exposure. The table only shows results for the second wave
(October-December) - that is when test data are available - on municipalities in all urban areas. The
first column only examines the poverty channel. Columns (2) to (4) respectively include one additional
variable capturing either the occupation or housing mechanism. The last column includes both the
poverty dummy and all the mechanism variables. All regressions include urban-area fixed-effects and
control for total population and for the share of inhabitants over 65 y.o. in the municipality. The
mechanism variables have been normalized such that coefficients can be interpreted in terms of the
effect of a one standard-deviation change, and can be compared with each other. The outcome-mean
line reports the mean of the incidence rate per 100K inhabitants (conditional on controls and fixed
effects) in each wave.

Figure 10: Effect of the index of frequent contact on excess mortality
rate over time
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on
the index of frequent contact, including controls for total population, share of the population over 65
y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate and the
index of frequent contact have been normalized. Confidence intervals at the 95% level.

Figure 11: Effect of the share of essential workers on excess mortality
rate over time
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on
the share of essential workers, including controls for total population, share of the population over 65
share of essential workers have been normalized. Confidence intervals at the 95% level.

Figure 12: Effect of the share of over-crowded housing on excess
mortality rate over time
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on the
share of over-crowded housing, including controls for total population, share of the population over 65
share of over-crowded housing have been normalized. Confidence intervals at the 95% level.

Figure 13: Effect of the share of multigenerational households on excess
mortality rate over time Back
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on the
share of multigenerational households, including controls for total population, share of the population
over 65 y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate
and the share of multigenerational households have been normalized. Confidence intervals at the 95%
level.

Spatial Inequalities during and after the COVID-19 Pandemic, Laura Khoury

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Spatial Inequalities during and after the COVID-19 Pandemic, Laura Khoury