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

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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 10/32
  12. 12. 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
  13. 13. 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
  14. 14. 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 13/32
  15. 15. 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
  16. 16. Empirical analysis Figure 3: Monthly difference in all-cause excess mortality by income 15/32
  17. 17. 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
  18. 18. 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
  19. 19. 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 18/32
  20. 20. 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
  21. 21. 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
  22. 22. Identification strategy - Green vs Red Figure 4: High- (red) and low-infection (green) Départements as of May 7, 2020 21/32
  23. 23. Identification strategy - Green vs Red 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
  24. 24. 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
  25. 25. 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
  26. 26. 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 25/32
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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) Urban areas FE X X X X X Controls X X X X X Control outcome mean 4.870 4.882 4.868 4.879 4.858 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
  32. 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 31/32
  33. 33. 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
  34. 34. Abedi, Vida, Oluwaseyi Olulana, Venkatesh Avula, Durgesh Chaudhary, Ayesha Khan, Shima Shahjouei, Jiang Li, and Ramin Zand, “Racial, Economic, and Health Inequality and COVID-19 Infection in the United States,” Journal of Racial and Ethnic Health Disparities, September 2020. Alfani, Guido, “Epidemics, Inequality, and Poverty in Preindustrial and Early Industrial Time,” Journal of Economic Literature, 2021. Almagro, Milena and Angelo Orane-Hutchinson, “The Determinants of the Differential Exposure to COVID-19 in New York City and Their Evolution Over Time,” Covid Economics: Vetted and Real-Time Papers, 2020, (13). Ashraf, Badar Nadeem, “Socioeconomic conditions, government interventions and health outcomes during COVID-19,” Covid Economics, 2020, 37, 141–162.
  35. 35. Beach, Brian, Karen Clay, and Martin H Saavedra, “The 1918 influenza pandemic and its lessons for COVID-19,” Journal of Economic Literature, 2021. Bras, Hervé Le, “L’épidémie et son terrain social,” June 2020. Library Catalog: jean-jaures.org. Brodeur, Abel, Nikolai Cook, and Taylor Wright, “On the effects of COVID-19 safer-at-home policies on social distancing, car crashes and pollution,” Journal of Environmental Economics and Management, March 2021, 106, 102427. Bullinger, Lindsey Rose, Jillian B. Carr, and Analisa Packham, “COVID-19 and Crime: Effects of Stay-at-Home Orders on Domestic Violence,” Technical Report w27667, National Bureau of Economic Research August 2020. Case, Anne, Darren Lubotsky, and Christina Paxson, “Economic status and health in childhood: The origins of the gradient,” American Economic Review, 2002, 92 (5), 1308–1334.
  36. 36. Chen, Jarvis T. and Nancy Krieger, “Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses,” Journal of Public Health Management and Practice, January 2021, 27 (1), S43–S56. Chetty, Raj, Michael Stepner, Sarah Abraham, Shelby Lin, Benjamin Scuderi, Nicholas Turner, Augustin Bergeron, and David Cutler, “The Association Between Income and Life Expectancy in the United States, 2001-2014,” JAMA, April 2016, 315 (16), 1750–1766. Currie, Janet, Hannes Schwandt, and Josselin Thuilliez, “Pauvreté, Egalité, Mortalité: mortality (in) equality in France and the United States,” Journal of Population Economics, 2020, 33 (1), 197–231. Cutler, David, Angus Deaton, and Adriana Lleras-Muney, “The determinants of mortality,” Journal of economic perspectives, 2006, 20 (3), 97–120.
  37. 37. Decoster, André, Thomas Minten, and Johannes Spinnewijn, “The income gradient in mortality during the Covid-19 crisis: evidence from Belgium,” Journal of Economic Inequality, forthcoming. Fouillet, Anne, Isabelle Pontais, and Céline Caserio-Schönemann, “Excess all-cause mortality during the first wave of the COVID-19 epidemic in France, March to May 2020,” Eurosurveillance, 2020, 25 (34), 2001485. Glaeser, Edward L, Caitlin S Gorback, and Stephen J Redding, “How much does COVID-19 increase with mobility? Evidence from New York and four other US cities,” Technical Report, National Bureau of Economic Research 2020. Jung, Juergen, James Manley, and Vinish Shrestha, “Coronavirus Infections and Deaths by Poverty Status: The Effects of Social Distancing,” SSRN Scholarly Paper ID 3623430, Social Science Research Network, Rochester, NY June 2020.
  38. 38. Mulligan, Casey B, “Deaths of Despair and the Incidence of Excess Mortality in 2020,” Technical Report, National Bureau of Economic Research 2021. Raifman, Matthew A. and Julia R. Raifman, “Disparities in the Population at Risk of Severe Illness From COVID-19 by Race/Ethnicity and Income,” American Journal of Preventive Medicine, July 2020, 59 (1), 137–139. Wiemers, Emily E, Scott Abrahams, Marwa AlFakhri, V Joseph Hotz, Robert F Schoeni, and Judith A Seltzer, “Disparities in Vulnerability to Severe Complications from COVID-19 in the United States,” Technical Report, National Bureau of Economic Research 2020.
  39. 39. Appendix
  40. 40. Table 4: Excess mortality rate by municipality income Back (1) (2) (3) (4) 2020 Wave 1 Wave 2 No wave 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 Urban area FE. 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.
  41. 41. Table 5: Excess mortality rate by municipality income 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
  42. 42. 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.
  43. 43. Table 6: No elderly care homes Back (1) (2) (3) (4) 2020 Wave 1 Wave 2 No wave Q1 (poor) 2.524** 1.021 1.139*** 0.363 (1.004) (0.648) (0.310) (0.327) Controls X X X X Urban area FE. X X X X Non poor average 6.486 2.784 3.831 -0.129 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 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.
  44. 44. 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.
  45. 45. Identification strategy - Green vs Red 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 * p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the urban-area level. NOTE: Mortality rates are expressed as number of deaths per 10,000 inhabitants.
  46. 46. 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.
  47. 47. 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.
  48. 48. 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) Urban areas FE X X X X X Controls X X X X X Control outcome mean 236.111 236.277 236.096 237.359 237.241 Adjusted R2 0.6922 0.6928 0.6934 0.6990 0.6997 Observations 16267 16267 16267 16267 16267 * p<0.1, ** p<0.05, *** p<0.01. Standard errors in parentheses clustered at the urban-area level. 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.
  49. 49. 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.
  50. 50. 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 y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate and the share of essential workers have been normalized. Confidence intervals at the 95% level.
  51. 51. 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 y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate and the share of over-crowded housing have been normalized. Confidence intervals at the 95% level.
  52. 52. 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.

Webinar Series on COVID-19 and Inequality, 8 July 2021, More information at: https://www.oecd.org/wise/events/covid-and-inequality-webinars.htm

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