# TSE Inaugural Lecture 2020: What Can History Teach Us About the Potential Consequences of COVID-19?

Victor Gay

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Victor Gay. TSE Inaugural Lecture 2020: What Can History Teach Us About the Potential Consequences of COVID-19?. Master. Inaugural Lecture, France. 2020. hal-02931899v2

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# What Can History Teach Us About the Potential Consequences of COVID-19?

Victor Gay

University of Toulouse 1 Capitole

Toulouse School of Economics

Institute for Advanced Study in Toulouse

September 2020

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Toulouse
School of
Economics
* In UT1 since September 2018:

* Assistant Professor at TSE.

* Research Fellow at IAST.

* Education: University of Chicago 2018.

* Fields: economic history, economics of culture, political economy.

* Dissertation: "The Legacy of the Missing Men: World War I and Female Labor in France Over a Century."
* Teaching in 2020–2021:

* M1 S1: Economic History.

* M1 S1: Applied Econometrics + group supervision.

* M2 PPD S2: Institutions and Long-Run Development.

* M2 ETE S2: Development (Part 2: Economic History, with M. Saleh).

* DEEQA S2: Natural Experiments in History (with M. Saleh).

* Supervision of M1 and M2 theses.

* Contact:

* Email: victor.gay@tse-fr.eu.

* Office: T.458.

* Website: victorgay.me
What can history teach us about the potential consequences of COVID-19?
* Most serious health and economic crisis of 21st century:

* Since December 2019: nearly 900k fatalities, 26M cases.

* No natural immunity, no cure yet.

* Large-scale non-pharmaceutical interventions.

* Large economic downturn, e.g., France: 2020 Q2/2019 Q2: -19%.

* Euro area 2020 GDP forecast: -11% to -9%.

⇒ Critical juncture.
* Most serious health and economic crisis of 21st century:

* Since December 2019: nearly 900k fatalities, 26M cases.

* No natural immunity, no cure yet.

* Large-scale non-pharmaceutical interventions.

* Large economic downturn, e.g., France: 2020 Q2/2019 Q2: -19%.

* Euro area 2020 GDP forecast: -11% to -9%.

⇒ Critical juncture.

* Economic history can some provide guidance.
Does the past have useful economics?
* More facts about economic growth

* Penn World Tables: real GDP PPP 182 countries since 1950.

* Maddison project database: real GDP since 1000.

* More facts about populations:

* IPUMS USA: full count censuses 1850–1950.

* IPUMS NAPP: full count censuses North Atlantic starting 1703.
(Often) Better Economic Facts
* Populations: no anonymity rule.

* IPUMS USA: linked samples across censuses.

* TRA France: linked families (vital and inheritance records).

* Full companies' records and manufacturing censuses.
* Populations: no anonymity rule.

* IPUMS USA: linked samples across censuses.

* TRA France: linked families (vital and inheritance records).

* Full companies' records and manufacturing censuses.

* Cautious approach to data as constructed objects.
* Economic history to build economic theory.

* Kaldor stylized facts and neoclassical growth.

* Ricardo and rising land rents.
* Economic history to build economic theory.

* Kaldor stylized facts and neoclassical growth.

* Ricardo and rising land rents.

* Economic history to test economic theory.

* Variety of past experiences and policy responses.

* Better identification through exogenous variation.
* Learn about mechanisms through institutional knowledge.
* Learn about mechanisms through institutional knowledge.

* Analyze long-run processes.
* Learn about mechanisms through institutional knowledge.

* Analyze long-run processes.

* Answer the big questions.
* Learn about mechanisms through institutional knowledge.

* Analyze long-run processes.

* Answer the big questions.

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McCloskey (1976) Abramitzky (2015) Cantoni and Yuchtman (2020)
* Economics emphasizes generality: the “imperial science”?
  Stigler (1984)

* Substitution and income effects.

* Equilibrium responses.

* Law of Demand.
* Economics emphasizes generality: the “imperial science”?
  Stigler (1984)

• Substitution and income effects.

* Equilibrium responses.

* Law of Demand.

* Reality of economic mechanisms is historically conditioned.
  Arrow (1985)

* Context is crucial to determine equilibrium responses.
* Characterize features COVID-19 crisis.

* Find commonalities (and differences) with past crises.

* Leverage past experiences.

* Useful references: Alfani and Murphy (2017) Arthi and Parman (2020) Beach, Clay, and Saavedra (2020)
* Features of COVID-19 in Historical Perspective

* Lessons from the Influenza Pandemic of 1918–1919

* Lessons from the Great Depression of the 1930s
COVID-19 in historical perspective
* Epidemiology of COVID-19.

* Demographic and distributional effects.

* Policy responses.

⇒ Characterization will generate natural historical counterparts.
* Epidemiology of COVID-19.

* Demographic and distributional effects.

* Policy responses.

⇒ Characterization will generate natural historical counterparts.

* Health effects: Influenza (1918–1919).

* Economic effects: Great Depression (1930s).
→ Epidemic risks were (nearly) part of everyday life.

<table><thead><tr><td>Epidemics</td><td>Period</td><td>Regions</td><td>Deaths</td></tr></thead><tbody><tr><td>Antonine Plague</td><td>160–180</td><td>Roman Empire</td><td>10–30%</td></tr><tr><td>Plague of Cyprian</td><td>249–270</td><td>Roman Empire</td><td>15–25%</td></tr><tr><td>Justinian plague</td><td>540–541</td><td>Eu., Med.</td><td>25–50M, 25–50%</td></tr><tr><td>Black Death</td><td>1347–1352</td><td>Eu. to Asia</td><td>~50M, 35–60% (Eu)</td></tr><tr><td>Italian plague</td><td>1629–1632</td><td>North. Italy</td><td>2M, 30–35%</td></tr><tr><td>Columbian Exchange</td><td>1492–1650</td><td>Americas</td><td>80–90%</td></tr><tr><td>Cholera Pandemics 1–6</td><td>1817–1923</td><td>Worldwide</td><td>1M+</td></tr><tr><td>Third Plague</td><td>1850s–1900s</td><td>China, India</td><td>12M</td></tr><tr><td>Russian Flu</td><td>1889–1890</td><td>Russia, Asia, Eu.</td><td>1M</td></tr></tbody></table>

## Some Major Historical Epidemics

Data from Alfani and Murphy (2017)
Comparison Set

<figure><img src="image_7.png" /><figcaption>Major 19th-century Epidemics in NYC</figcaption></figure>

Figure from Beach, Clay, and Saavedra (2020) using data from Rosenwaike (1972) and other primary sources.
COVID-19 data are provisional
* Very rapid and global diffusion (World Mapper's dynamic cartogram).
Epidemiology of COVID-19: Spatial Diffusion

* Very rapid and global diffusion (World Mapper's dynamic cartogram).

* Similar patterns in past pandemics: along trade routes, trading centers (Amsterdam, London, Venice...).

* Most common with worldwide spread of 1918–1919 Influenza.
Epidemiology of COVID-19: Spatial Diffusion

<figure><img src="image_8.png" /><figcaption>Worldwide Spread of 1918–1919 Influenza Pandemic</figcaption></figure>

Data for 48 countries from Barro, Ursua, and Weng (2020).
* Basic reproductive rate ($R_0$): average number of secondary infections produced by a typical case in a population where everyone is susceptible.

* Estimates: 1.8–3.6, though debated. Petersen et al. (2020)

* Comparable to 1918 Influenza, yellow fever, or SARS: ~3.

* Much lower than measles, with $R_0$ of above 10.
# Epidemiology of COVID-19: Rate of Transmission

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## Basic Reproduction Rates and Case Fatality Rates Across Epidemics

Data from Arthi and Parman (2020).
* Measuring COVID-19 severity while unfolding is challenging:

* Case fatality rate: death from COVID / diagnosed cases of COVID.

* Crude mortality rate: death from COVID / total population.

* Infection fatality rate: death from COVID / cases of COVID.
* Measuring COVID-19 severity while unfolding is challenging:

* Case fatality rate: death from COVID / diagnosed cases of COVID.

* Crude mortality rate: death from COVID / total population.

* Infection fatality rate: death from COVID / cases of COVID.

* Current average worldwide CFR ≈ 3% (variation across time/space).
* Measuring COVID-19 severity while unfolding is challenging:

* Case fatality rate: death from COVID / diagnosed cases of COVID.

* Crude mortality rate: death from COVID / total population.

* Infection fatality rate: death from COVID / cases of COVID.

* Current average worldwide CFR ≈ 3% (variation across time/space).

* Influenza 1918-1919 CFR ≈ 2.5%.

* Much lower than Ebola, or smallpox.
* Measuring COVID-19 severity while unfolding is challenging:

* Case fatality rate: death from COVID / diagnosed cases of COVID.

* Crude mortality rate: death from COVID / total population.

* Infection fatality rate: death from COVID / cases of COVID.

* Current average worldwide CFR ≈ 3% (variation across time/space).

* Influenza 1918-1919 CFR ≈ 2.5%.

* Much lower than Ebola, or smallpox.

⇒ Initial spread, transmission, severity comparable to Influenza.
# Epidemiology of COVID-19: Rate of Transmission

<figure><img src="image_10.png" /><figcaption>Case Fatality Rates</figcaption></figure>

Data from ECDC and ourworldindata.org.
<figure><img src="image_11.png" /><figcaption>Excess Mortality in 2020 vs 2015–2019 by Age in France</figcaption></figure>
* Old age and worse baseline health.
* Old age and worse baseline health.

* Low SES: compounded risks.

* High contact jobs, continuing during pandemic.

* Likely low-paying jobs, more likely women and minorities.

* Differential access to healthcare.
* Old age and worse baseline health.

* Low SES: compounded risks.

* High contact jobs, continuing during pandemic.

* Likely low-paying jobs, more likely women and minorities.

* Differential access to healthcare.

* Low SES more affected during Influenza (more later).
* Old age and worse baseline health.

* Low SES: compounded risks.

* High contact jobs, continuing during pandemic.

* Likely low-paying jobs, more likely women and minorities.

* Differential access to healthcare.

* Low SES more affected during Influenza (more later).

* Difference with Influenza: W shape in age susceptibility.
Demographic and Distributional Effects

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Median Excess Mortality Rate by Age and Sex (13 countries).

Figure from Murray et al. (2006)
* Absent of cure and limited medical capacities:
  ⇒ Widespread non-pharmaceutical interventions (NPIs).
* Absent of cure and limited medical capacities:
  ⇒ Widespread non-pharmaceutical interventions (NPIs).

* Shutdowns

* Travel restrictions

* Quarantines

* Social distancing

* Mask wearing

* Open, linked, updated dataset Desvars-Larrive et al. (2020) *Sci Data*
* Absent of cure and limited medical capacities:
  ⇒ Widespread non-pharmaceutical interventions (NPIs).

* Shutdowns

* Travel restrictions

* Quarantines

* Social distancing

* Mask wearing

* Open, linked, updated dataset Desvars-Larrive et al. (2020) *Sci Data*

* Quarantines very common in the past, some NPI during Influenza.

* Generally not as stringent and generalized as today.
Lessons from the Influenza Pandemic of 1918–1919
* Key features:

* Brief: Spring 1918–early 1919 (last wave in 1920 in some countries).

* Worldwide diffusion (with substantial variation).

* Severe fatalities: 20–50M deaths.

* Some countries implemented NPIs.
* Key features:

* Brief: Spring 1918–early 1919 (last wave in 1920 in some countries).

* Worldwide diffusion (with substantial variation).

* Severe fatalities: 20–50M deaths.

* Some countries implemented NPIs.

* Useful to think about some potential consequences of COVID–19.

* Short-run health and economic consequences.

* Medium-run economic effects of NPIs.

* Long-run health consequences.
* Measurement of mortality.

* Limited capacities for classification.

* Political pressure and timely information in wartime.

⇒ Scholars rely on excess mortality from all causes.
* Measurement of mortality.

* Limited capacities for classification.

* Political pressure and timely information in wartime.

⇒ Scholars rely on excess mortality from all causes.

* Simultaneity of WWI (e.g., France).

* 180k civilians, 30k soldiers. Darmon (2000) Guenel (2004)

* Civilian fatalities: 74 départements (*Statistique Sanitaire de la France*)

* Military fatalities: no disaggregation (*Statistique Médicale*).

* No correlation with military death rates. Boehnke and Gay (2020)
# What Explains Distribution of Influenza Fatalities?
# What Explains Distribution of Influenza Fatalities?

## Clay, Lewis, and Severnini (2019a)

* Focus on U.S. case: better data, no WWI on soil.

* Total death toll: 600–700k in 1918.

* Spread from WWI bases and through troops movements.

* Evidence from the 438 cities >10k residents (1915–1925).
* Measuring influenza mortality:

* Predict 1918 mortality using city linear trend over 1915–1925.

$$M_{ct} = \alpha_{c} + \beta_{c}t + \varepsilon_{ct}$$

* $\alpha_c$: city-specific intercept; $\beta_c$: city-specific trend; 1918 excluded.

* Excess mortality: $M_{c1918} - \hat{M}_{c1918}$
* Measuring influenza mortality:

* Predict 1918 mortality using city linear trend over 1915–1925.

$$M_{ct} = \alpha_c + \beta_c t + \varepsilon_{ct}$$

* $\alpha_c$: city-specific intercept; $\beta_c$: city-specific trend; 1918 excluded.

* Excess mortality: $M_{c1918} - \hat{M}_{c1918}$

* Similar results if predict 1918 with 1915–1917 data.
# What Explains Distribution of Influenza Fatalities?

<figure><img src="image_13.png" /><figcaption>(a) Mortality, 1915-1925</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(b) Excess Mortality in 1918</figcaption></figure>

## All-age Mortality Across U.S. Cities

Figure from Clay, Lewis, and Severnini (2019a)
# What Explains Distribution of Influenza Fatalities?

* Pre-pandemic city-level characteristics analyzed:

* Health conditions: infant mortality (1915–1916).

* Economic conditions: illiteracy rate (1910).

* Timing of pandemic: proximity to WWI base.

* Environmental pollution: coal-fired electricity capacity.
# What Explains Distribution of Influenza Fatalities?

* Pre-pandemic city-level characteristics analyzed:

* Health conditions: infant mortality (1915–1916).

* Economic conditions: illiteracy rate (1910).

* Timing of pandemic: proximity to WWI base.

* Environmental pollution: coal-fired electricity capacity.

* Regress excess mortality on these characteristics + share of urban population through a multivariate regression.
# What Explains Distribution of Influenza Fatalities?

<figure><img src="image_15.png" /><figcaption>Determinants of Excess Mortality in 1918</figcaption></figure>

Figure from Beach, Clay, and Saavedra (2020) using estimates from Clay, Lewis, and Severnini (2019)
# What Explains Distribution of Influenza Fatalities?

* Influenza more deadly in locations with worse baseline:

* Health environment Acuna-Soto, Vibond, and Chowell (2011) Bootsma and Ferguson (2007)

* Overall income Grantz et al. (2016) Tuckel et al. (2016)

* Air quality Clay, Lewis, and Severini (2019b)

⇒ Aggravated pre-pandemic socioeconomic disparities.
# What Explains Distribution of Influenza Fatalities?

* Influenza more deadly in locations with worse baseline:

* Health environment Acuna-Soto, Vibond, and Chowell (2011) Bootsma and Ferguson (2007)

* Overall income Grantz et al. (2016) Tuckel et al. (2016)

* Air quality Clay, Lewis, and Severini (2019b)

⇒ Aggravated pre-pandemic socioeconomic disparities.

* Some issues:

* Mostly focus on U.S. case.

* Few individual-level studies.
# What Were the Economic Effects of Influenza?
# What Were the Economic Effects of Influenza?

* Short-run macroeconomic effects Barro, Ursua, and Weng (2020)

* Panel 43 countries (1901–1929). Flu death rates ≈ 2%

* Did flu deaths (and war deaths) generated differential changes across countries in rates of economic growth?

* ↑ 1pp flu death rates ⇒ ↓ 3% real GDP per K.

* In aggregate: pandemic ⇒ ↓ 6% real GDP per K.
# What Were the Economic Effects of Influenza?

* Short-run macroeconomic effects Barro, Ursua, and Weng (2020)

* Panel 43 countries (1901–1929). Flu death rates ≈ 2%

* Did flu deaths (and war deaths) generated differential changes across countries in rates of economic growth?

* ↑ 1pp flu death rates ⇒ ↓ 3% real GDP per K.

* In aggregate: pandemic ⇒ ↓ 6% real GDP per K.

* Medium-run economic effects (within-country analyzes):

* Italy: ↓ 6.5% real GDP, recover by 1922. Carillo and Jappelli (2020)

* Denmark: short-lived ↓ income growth. Dahl et al. (2020)

* Complicated by WWI deaths and war production (esp. in U.S.).
What Were the Economic Effects of NPIs?
# What Were the Economic Effects of NPIs?

* U.S. cities implemented non-pharmaceutical interventions (NPIs). (Markel et al. 2007)

* NPIs: school closures, quarantines, bans on public assemblies.

* Information for 43 cities with weekly influenza fatalities data.

* *NPI Intensity*: cumulative number of days with each NPI.

* More intensive toward the West.
# What Were the Economic Effects of NPIs?

<figure><img src="image_16.png" /><figcaption>NPI Intensity Across 43 U.S. Cities</figcaption></figure>

Figure from Correia, Luck, and Verner (2020) using data from Markel et al. (2007)
# What Were the Economic Effects of NPIs?

Correia, Luck, and Verner (2020)

* Health effects:

* NPIs flattened the mortality curve.

* High NPI cities: 45% ↓ in peak mortality relative to the mean.

* Is there a trade-off between economic and health effects?

* Positive effects: mitigates transmission and lowers mortality.

* Negative effects: short-run economic disruptions while in place.
# What Were the Economic Effects of NPIs?

Correia, Luck, and Verner (2020)

* Health effects:

* NPIs flattened the mortality curve.

* High NPI cities: 45% ↓ in peak mortality relative to the mean.

* Is there a trade-off between economic and health effects?

* Positive effects: mitigates transmission and lowers mortality.

* Negative effects: short-run economic disruptions while in place.

* Impact of NPIs on city-level economic activity.

* Manufacturing employment (1904, 1909, 1914, 1919–1927).

* Total national bank assets (1910–1925, annual frequency).
* Dynamic difference-in-differences specification:

$$Y_{ct} = \alpha_c + \tau_t + \sum_{j \neq 1914} \beta_j NPI_c 1_{j=t} + \sum_{j \neq 1914} X_c \gamma_j 1_{j=t} + \varepsilon_{ct}$$

* $\alpha_c$: city FE; $\tau_t$: year FE.

* $X_c$: city-level pre-pandemic population and employment controls.
* Dynamic difference-in-differences specification:

$$Y_{ct} = \alpha_c + \tau_t + \sum_{j \neq 1914} \beta_j NPI_c 1_{j=t} + \sum_{j \neq 1914} X_c \gamma_j 1_{j=t} + \varepsilon_{ct}$$

* $\alpha_c$: city FE; $\tau_t$: year FE.

* $X_c$: city-level pre-pandemic population and employment controls.

* Control for pre-treatment differences which relationship with outcomes might have changed over time.

* Faster growing cities (West) were more likely to implement NPIs.
* Dynamic difference-in-differences specification:

$$Y_{ct} = \alpha_c + \tau_t + \sum_{j \neq 1914} \beta_j NPI_c 1_{j=t} + \sum_{j \neq 1914} X_c \gamma_j 1_{j=t} + \varepsilon_{ct}$$

* $\alpha_c$: city FE; $\tau_t$: year FE.

* $X_c$: city-level pre-pandemic population and employment controls.

* Control for pre-treatment differences which relationship with outcomes might have changed over time.

* Faster growing cities (West) were more likely to implement NPIs.

* Discussions of pre-trends: Lilley et al. (2020) Correia et al. (2020)
What Were the Economic Effects of NPIs?

<figure><img src="image_17.png" /><figcaption>NPI Intensity and log manufacturing employment</figcaption></figure>

Figure from Correia, Luck, and Verner (2020)
# What Were the Economic Effects of NPIs?

<figure><img src="image_18.png" /></figure>

NPI Intensity and log national bank assets

Figure from Correia, Luck, and Verner (2020)
# What Were the Economic Effects of NPIs?

* Little evidence for negative economic effects of NPIs.

* NPIs did not disrupt economic activities (no business closures).

* If anything, positive effects: through mitigating pandemics?

* Highly dependent on context e.g., deadly to prime-age workers.
# Were There Scarring Effects of Influenza?
## Almond (2006)

* Long-run scarring effects through in utero exposure ("fetal origins")?

* Socioeconomic outcomes in 1960–1980 depending on birth quarter.
# Were There Scarring Effects of Influenza?

## Almond (2006)

* Long-run scarring effects through in utero exposure ("fetal origins")?

* Socioeconomic outcomes in 1960–1980 depending on birth quarter.

* Long-lasting compared to adjacent cohorts:

* Disability rates.

* Human capital accumulation.

* Income level.
<figure><img src="image_19.png" /><figcaption>1980 Male Disability Rates by Quarter of Birth</figcaption></figure>

Figure from Almond (2006)
# Were There Scarring Effects of Influenza?

<figure><img src="image_20.png" /><figcaption>1970 Disability Rates by Year of Birth</figcaption></figure>

Figure from Beach, Clay, and Saavedra (2020) using results from Almond (2006)
Were There Scarring Effects of Influenza?

<figure><img src="image_21.png" /></figure>

1970 High School Dropout Rates by Year of Birth

Figure from Beach, Clay, and Saavedra (2020) using results from Almond (2006)
Were There Scarring Effects of Influenza?

<figure><img src="image_22.png" /><figcaption>1970 Poverty Rates by Year of Birth</figcaption></figure>

Figure from Beach, Clay, and Saavedra (2020) using results from Almond (2006)
# Were There Scarring Effects of Influenza?

* Causal effect from Influenza exposure?

* Resources reallocation toward healthier siblings. Parman (2015)

* WWI veterans + selected ⇒ 1919 cohort from lower SES.
  Brown and Thomas (2018)

* Most results hold even with linked census data + DiD strategy.
  Beach, Ferrie, and Saavedra (2018)
# Were There Scarring Effects of Influenza?

* Causal effect from Influenza exposure?

* Resources reallocation toward healthier siblings. Parman (2015)

* WWI veterans + selected ⇌ 1919 cohort from lower SES. Brown and Thomas (2018)

* Most results hold even with linked census data + DiD strategy. Beach, Ferrie, and Saavedra (2018)

* Comparative evidence for Taiwan, Japan, Sweden, Switzerland, Brazil. Lin and Liu (2014) Ogasawara (2017) Helgeretz and Bengtsson (2019) Neelsen and Stratmann (2012) Guimbeau et al. (2020)
* Fertility:

* Drop in fertility during, baby boom after (e.g., Norway, Sweden).
Mamelund (2004) Boberg-Fazlic et al (2017)

* WWI as confounding factor.
Abramitzky et al. (2011) Boehnke and Gay (2020)
* Fertility:

* Drop in fertility during, baby boom after (e.g., Norway, Sweden).
    Mamelund (2004) Boberg-Fazlic et al (2017)

* WWI as confounding factor.
    Abramitzky et al. (2011) Boehnke and Gay (2020)

* Political economy

* Increased voting for extremist parties (e.g., Germany)? Blickle (2020)

* Lower trust in institutions and politicians ? Aassve et al. (2020)
# Other Influenza Effects

* Fertility:

* Drop in fertility during, baby boom after (e.g., Norway, Sweden).
    Mamelund (2004) Boberg-Fazlic et al (2017)

* WWI as confounding factor.
    Abramitzky et al. (2011) Boehnke and Gay (2020)

* Political economy

* Increased voting for extremist parties (e.g., Germany)? Blickle (2020)

* Lower trust in institutions and politicians ? Aassve et al. (2020)

* Inequality

* Increased inequality (e.g., Italy)? Galletta and Giommoni (2020)
# Other Influenza Effects

* Fertility:

* Drop in fertility during, baby boom after (e.g., Norway, Sweden).
    Mamelund (2004) Boberg-Fazlic et al (2017)

* WWI as confounding factor.
    Abramitzky et al. (2011) Boehnke and Gay (2020)

* Political economy

* Increased voting for extremist parties (e.g., Germany)? Blickle (2020)

* Lower trust in institutions and politicians ? Aassve et al. (2020)

* Inequality

* Increased inequality (e.g., Italy)? Galletta and Giommoni (2020)

* Migration

* Increased internal mobility (e.g., India)? Donaldson Keniston (2016)
* Human capital accumulation:

* School closures were less stringent than today.

* Baseline and returns to education were much lower.
* Human capital accumulation:

* School closures were less stringent than today.

* Baseline and returns to education were much lower.

* Gender inequality:

* Female labor force participation rates were much lower.

* Indirect effects through income shocks.
* Human capital accumulation:

* School closures were less stringent than today.

* Baseline and returns to education were much lower.

* Gender inequality:

* Female labor force participation rates were much lower.

* Indirect effects through income shocks.

* Effects of NPIs:

* Less stringent and less disruptive.
* Human capital accumulation:

* School closures were less stringent than today.

* Baseline and returns to education were much lower.

* Gender inequality:

* Female labor force participation rates were much lower.

* Indirect effects through income shocks.

* Effects of NPIs:

* Less stringent and less disruptive.

⇌ For economic effects, the Great Depression more useful.
Lessons from Great Depression of the 1930s
# Scarring Effects of the Great Depression?

<figure><img src="image_23.png" /></figure>

The Great Depression in the U.S.

<caption>Figure from Moulton (2017). Lebergott vs Darby: workers in government sponsored work programs considered unemployed.</caption>
# Scarring Effects of the Great Depression?

* Long-run scarring effects through in utero exposure to negative income shocks in Great Depression. Fishback and Thomasson (2014)
# Scarring Effects of the Great Depression?

* Long-run scarring effects through in utero exposure to negative income shocks in Great Depression. Fishback and Thomasson (2014)

* Labor market entry scarring. Moulton (2017)
# Scarring Effects of the Great Depression?

* Long-run scarring effects through in utero exposure to negative income shocks in Great Depression. Fishback and Thomasson (2014)

* Labor market entry scarring. Moulton (2017)

* Assess long-term impact of entering labor market in 1929–1930.

* Cohorts 1911–1920.

* RDD: compare entering before/after 1930 of 8th graders.

* Running variable: birth year with cutoff = 1916 assuming entry at 14.

* Outcome: income in 1940 census.
* RD specification:

$$Y_{isc} = \alpha + \beta_1 D_{isc} + \beta_2 \text{dist}_{1916_{isc}} + \beta_3 \text{dist}_{1916_{isc}} \times D_{isc} + BPL_s + \varepsilon_{isc}$$

* $D_{isc}$: 1 (birth\_year$_{isc}$ ≥ 1916).

* dist$_{1916_{isc}}$: birth\_year$_{isc}$ - 1916.

* BPL$_s$: birth state FE.

* $\beta_1$: discontinuity of entering labor market in 1929 vs 1930.

* $\beta_2$: trend in relationship between Y and birth year.

* $\beta_3$: differences in trends across cutoff.
# Scarring Effects of the Great Depression?

<figure><img src="image_24.png" /></figure>

## Discontinuity in labor market conditions

Figure from Moulton (2017).
# Scarring Effects of the Great Depression?

<figure><img src="image_25.png" /></figure>

Regression discontinuity of log(income).

Figure from Moulton (2017).
# Scarring Effects of the Great Depression?

* Earnings penalties (15%) for less-educated in high shock states.
# Scarring Effects of the Great Depression?

* Earnings penalties (15%) for less-educated in high shock states.

* Threats to identification: endogenous entry decision.

* Limited options to borrow and prolonging education.

* Little evidence of differences in educational attainment.

* Different to today.
# Scarring Effects of the Great Depression?

* Earnings penalties (15%) for less-educated in high shock states.

* Threats to identification: endogenous entry decision.

* Limited options to borrow and prolonging education.

* Little evidence of differences in educational attainment.

* Different to today.

* Occupational and residential displacement of younger cohorts.

Arthi, Eriksson, and Richardson (2020)

* Small silver-lining: move to later dynamic sectors.
* Another historical case: 2007–2012 Great Recession.
# Scarring Effects of the Great Recession?

* Another historical case: 2007–2012 Great Recession.

* No long-run perspective, but some evidence (US). Rothstein (2020)

* 2009–2015 entrants have 2–3% lower employment rates compared to pre-crisis entrants.

* Only 2009 entrants have lower earnings (2%).

* Employment scarring permanent, wage scarring temporary.

* Employment break for entrants began in 2005.

⇒ If health-economy trade-off, especially acute in current context.
Conclusion
* History can be a useful guide for policy.

* Absent of interventions:

* Low SES individuals bear disproportionate burden of health crisis.

* Labor market entrants bear disproportionate burden of economic crisis.

* Long-lasting scarring effects.
* History can be a useful guide for policy.

* Absent of interventions:

* Low SES individuals bear disproportionate burden of health crisis.

* Labor market entrants bear disproportionate burden of economic crisis.

* Long-lasting scarring effects.

* More historical evidence needed:

* Original features of COVID-19: combines health and economic crises.
    ⇒ How will these interact?
* History can be a useful guide for policy.

* Absent of interventions:

* Low SES individuals bear disproportionate burden of health crisis.

* Labor market entrants bear disproportionate burden of economic crisis.

* Long-lasting scarring effects.

* More historical evidence needed:

* Original features of COVID-19: combines health and economic crises.
    ⇒ How will these interact?

* Scarring effects for young children (school closures, distancing, masks)?

* Consequences for gender inequality, esp. working mothers?
Thank you for your attention!
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