# Context 1: Political Fragmentation

* High political fragmentation $\implies$ high potential for exit: $m > 0$.

* Multidirectional attacks $\implies$ high value of loyalty: $l \gg 0$.

* Solve the game by backward induction:

* $m > 0 \implies$ elites move rather than stay in last stage.

* $l \gg 0 \implies$ ruler grants rather than deny ($l - p > 0$).

* If coerced and moving not too attractive ($p - c_E > m > 0$), elites contest.

* If not coerced and value of bargain not too low ($p > m$), elites stay.
# Context 1: Political Fragmentation

* High political fragmentation $\implies$ high potential for exit: $m > 0$.

* Multidirectional attacks $\implies$ high value of loyalty: $l \gg 0$.

* Solve the game by backward induction:

* $m > 0 \implies$ elites move rather than stay in last stage.

* $l \gg 0 \implies$ ruler grants rather than deny ($l - p > 0$).

* If coerced and moving not too attractive ($p - c_E > m > 0$), elites contest.

* If not coerced and value of bargain not too low ($p > m$), elites stay.

* Ruler always bargains ($c_R > 0$).
# Context 1: Political Fragmentation

* High political fragmentation $\implies$ high potential for exit: $m > 0$.

* Multidirectional attacks $\implies$ high value of loyalty: $l \gg 0$.

* Solve the game by backward induction:

* $m > 0 \implies$ elites move rather than stay in last stage.

* $l \gg 0 \implies$ ruler grants rather than deny ($l - p > 0$).

* If coerced and moving not too attractive ($p - c_E > m > 0$), elites contest.

* If not coerced and value of bargain not too low ($p > m$), elites stay.

* Ruler always bargains ($c_R > 0$).

* Subgame perfect equilibrium: (Bargain, Grant; Contest, Stay, Move).
* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

* Unidirectional attacks $\Rightarrow$ low value of loyalty: $l > 0$.
Context 2: Political Centralization

• Low political fragmentation ⇌ low potential for exit: $m < 0$.

• Unidirectional attacks ⇌ low value of loyalty: $l > 0$.

• Solve the game by backward induction:
* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

* Unidirectional attacks $\Rightarrow$ low value of loyalty: $l > 0$.

* Solve the game by backward induction:

* $m < 0 \Rightarrow$ elites stay rather than move in last stage.
* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

* Unidirectional attacks $\Rightarrow$ low value of loyalty: $l > 0$.

* Solve the game by backward induction:

* $m < 0 \Rightarrow$ elites stay rather than move in last stage.

* $p > 0 \Rightarrow$ ruler denies rather than grant.
* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

* Unidirectional attacks $\Rightarrow$ low value of loyalty: $l > 0$.

* Solve the game by backward induction:

* $m < 0 \Rightarrow$ elites stay rather than move in last stage.

* $p > 0 \Rightarrow$ ruler denies rather than grant.

* If coerced, elites stay rather than move or contest ($m < 0$ and $c_E > 0$).

* If not coerced, elites stay rather than move ($m < 0$ and $p > 0$).
* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

* Unidirectional attacks $\Rightarrow$ low value of loyalty: $l > 0$.

* Solve the game by backward induction:

* $m < 0 \Rightarrow$ elites stay rather than move in last stage.

* $p > 0 \Rightarrow$ ruler denies rather than grant.

* If coerced, elites stay rather than move or contest ($m < 0$ and $c_E > 0$).

* If not coerced, elites stay rather than move ($m < 0$ and $p > 0$).

* Ruler always coerces ($p > 0$).
* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

* Unidirectional attacks $\Rightarrow$ low value of loyalty: $l > 0$.

* Solve the game by backward induction:

* $m < 0 \Rightarrow$ elites stay rather than move in last stage.

* $p > 0 \Rightarrow$ ruler denies rather than grant.

* If coerced, elites stay rather than move or contest ($m < 0$ and $c_E > 0$).

* If not coerced, elites stay rather than move ($m < 0$ and $p > 0$).

* Ruler always coerces ($p > 0$).

* Subgame perfect equilibrium: (Coerce, Deny; Stay, Stay, Stay).
## Exit, voice, loyalty: Europe versus China

<table><thead><tr><td>Attribute</td><td>Europe</td><td>China</td></tr></thead><tbody><tr><td>Political geography</td><td>Fragmentation</td><td>Centralization</td></tr><tr><td>State size</td><td>Small</td><td>Large</td></tr><tr><td>Exit ability<sup>a</sup></td><td>High</td><td>Low</td></tr><tr><td>Warfare</td><td>Common</td><td>Common</td></tr><tr><td>Conflict type</td><td>External</td><td>Internal</td></tr><tr><td>Threat direction<sup>b</sup></td><td>Multidirectional</td><td>Unidirectional</td></tr><tr><td>Power balance</td><td>Favors elite</td><td>Favors ruler</td></tr><tr><td>Political representation</td><td>More likely</td><td>Less likely</td></tr></tbody></table>

Source: Dincecco and Wang (2018)
* In Europe:

* Political representation helped build fiscal capacity.

* Elites more likely to agree on taxes if exert control.
* In Europe:

* Political representation helped build fiscal capacity.

* Elites more likely to agree on taxes if exert control.

* In China:

* No representation so little agreement on taxes.

* Vast geography ⇒ communication costs and difficult monitoring.
<figure><img src="image_341.png" /><figcaption>Per capita revenue across Eurasia in the 1780s</figcaption></figure>
* Does this logic apply in Sub-Saharan Africa?
* Does this logic apply in Sub-Saharan Africa?

* Three region-specific factors break the war-state logic.

* Political geography.

* Transatlantic slave trade.

* European colonization.
* High land-labor ratio ⇒ little incentives to control territory.

* Mostly raiding wars: much less expensive.

* Little incentives to extract revenues and build fiscal capacity.
* Transatlantic slave trade.

* Generated outgrowth of pre-colonial conflicts over people.

* Accentuated nature of intra-African wars.

* Slaves more valuable than taxes ⇌ ↓ fiscal capacity.
# Slave Trade and Colonization

* Transatlantic slave trade.

* Generated outgrowth of pre-colonial conflicts over people.

* Accentuated nature of intra-African wars.

* Slaves more valuable than taxes ⇒ ↓ fiscal capacity.

* European colonization.

* Scramble for Africa created conditions for persistence of conflict.

* Arbitrary borders ↑ fractionalization ⇒ persistent civil conflicts.
# Slave Trade and Colonization

* Transatlantic slave trade.

* Generated outgrowth of pre-colonial conflicts over people.

* Accentuated nature of intra-African wars.

* Slaves more valuable than taxes ⇌ ↓ fiscal capacity.

* European colonization.

* Scramble for Africa created conditions for persistence of conflict.

* Arbitrary borders ↑ fractionalization ⇌ persistent civil conflicts.

* Civil conflicts ⇌ capture of state by specific groups

* Little incentives to invest in generalized particularized institutions.
* Empirical analysis in Dincecco, Fenske, and Onorato (2019).
* Empirical analysis in Dincecco, Fenske, and Onorato (2019).

* Historical conflict data.

* Conflicts in Old World (drop Americas). Brecke (1999)

* Assign conflicts to modern borders.

* Main variable: share of years with conflict on soil 1400–1799 (traditional warfare).
* Empirical analysis in Dincecco, Fenske, and Onorato (2019).

* Historical conflict data.

* Conflicts in Old World (drop Americas). Brecke (1999)

* Assign conflicts to modern borders.

* Main variable: share of years with conflict on soil 1400–1799 (traditional warfare).

* Civil conflict: share of years in civil war 1950–2000.

Besley and Persson (2011)
* Empirical analysis in Dincecco, Fenske, and Onorato (2019).

* Historical conflict data.

* Conflicts in Old World (drop Americas). Brecke (1999)

* Assign conflicts to modern borders.

* Main variable: share of years with conflict on soil 1400–1799 (traditional warfare).

* Civil conflict: share of years in civil war 1950–2000. Besley and Persson (2011)

* Fiscal capacity (1990–2014).

* Main variable: share of income taxes in total taxes. IMF

* Income tax require large administrative capacity.

* Also: share of direct taxes, income taxes to GDP, tax revenues to GDP.
<figure><img src="image_342.png" /><figcaption>Historical conflict, 1400–1799</figcaption></figure>
$$y_i = \alpha + \beta \text{ Conflict}_i + \delta \text{ Conflict}_i \times \text{Africa} + x'_i\gamma + \mu_j + \varepsilon_i$$

* i indexes countries.

* $y_i$: measure of fiscal capacity today.

* $\text{Conflict}_i$: measure of historical conflict.

* $x'_i$: vector of controls (population density in 1500, timing of Neolithic Revolution, land suitability for agriculture, log absolute latitude, total land area).

* $\mu_j$: FE for Asia, Europe, North Africa, Sub-Saharan Africa.

* $\delta$: measures how relationship for Africa differs from $\beta$.
Warfare and Political Development: Results

<table><thead><tr><th rowspan="2">Dependent variable:</th><th>Income tax share</th><th>Direct tax share</th><th>Income tax to GDP</th><th>Total tax to GDP</th><th>Civil war share</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Conflict (1400–1799)</td><td>0.33***<br>[0.11]</td><td>0.60**<br>[0.28]</td><td>0.07*<br>[0.03]</td><td>0.05<br>[0.05]</td><td>0.02<br>[0.183]</td></tr><tr><td>Conflict × Africa</td><td>-0.06<br>[0.24]</td><td>1.32<br>[3.07]</td><td>0.12<br>[0.13]</td><td>0.27<br>[0.40]</td><td>2.75***<br>[1.21]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries</td><td>110</td><td>37</td><td>110</td><td>118</td><td>116</td></tr></tbody></table>

* ↑ 1 s.d. conflict in Old World ⇒ ↑ 0.4 s.d. income tax share.

* Fiscal strength rather than government size.

* Not in Africa + no “anti-persistence” of conflict.
* Additional controls: colonizer, artificial borders, legal origin, state antiquity, geographical conditions.

* Test for OVB. Altonji et al. (2005)

* Alternative samples:

* Drop 18th century wars, Russia, China.

* Add New World.

* Intermediate outcomes:

* Infrastructural power of state (railroads in 1910).

* Share of years with conflict 1850–1899.
A Culture of Growth

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_343.png" /></figure>

<figure><img src="image_344.png" /></figure>

<figure><img src="image_345.png" /></figure>

Toulouse
School of
Economics
<figure><img src="image_346.png" /><figcaption>Patents per year in England (1660–1851)</figcaption></figure>

Source: Clark (2014) based on Mitchell (1988).
"A wave of gadgets swept over England." *Ashton (1948)*

* Textile sector:

* Hargreaves spinning jenny (mid-1760s)

* Arkwright water frame (late 1760s)

* Crompton mule jenny (late 1770s)

* Steam power:

* Newcomen engine (1710s)

* Watt steam engine (1760s)

⇒ Wave of simultaneous innovations.
<figure><img src="image_347.png" /><figcaption>Spinning jenny</figcaption></figure>
<figure><img src="image_348.png" /><figcaption>Cotton spinning and weaving productivity (1770–1869)</figcaption></figure>

Source: Clark (2014) based on Harley (1998).
Cultural roots of the Great Enrichment
# The Enlightenment

Mokyr (2009, 2016, 2021) McClosley (2016)

* Crucial change: ideas and culture of Enlightenment.

* Ideas of progress 18th century Northwest Europe.

* Mankind can improve condition through science and rationality.

* Especially in Britain: advocate empiricism (e.g., Bacon).

* Precursor to Industrial Revolution?

* More than science, a culture of useful knowledge?
# The Enlightenment

* Before: inferiority complex with respect to ancient wisdom that impeded scientific and technological progress, even accusations of heresy.

* After 1500 in Europe: Renaissance scholars rediscovered ancient scientific writings.

* By late 17th century, skepticism and intellectual deference largely gone (e.g., Newton physics).
* From late 15th and great voyages, new scientific instruments and tools, confrontation of old wisdom with discoveries (Copernicus, Galileo, ...).

* Institutional change (Reformation) and improved technology (printing press) encouraged rapid proliferation of intellectual innovations, creating a market for ideas.

* 16th and 17th century Europe:

* Large demand for scientific innovation through patronage of leading scholars of princes and the wealthy, and universities and academies.

* Applications of innovations to military matters or tutors for princes: highly competitive demand side.

* Highly competitive supply side for wealth and rise in social standing, and open evaluation of reputation through the Republic of Letters.
## The academic market in Europe (1088–1800)

<figure><img src="image_349.png" /></figure>

Source: De La Croix et al. (2023). Map of the university-scholar dyads over time. Red dots correspond to universities. Blue dots represent scholars' birthplaces. Size of blue dots are function of publications. The dashed lines link academic scholars' birth place to the university for which they taught through the least costly path. Video: https://perso.uclouvain.be/david.delacroix/video/test2.mp4
* A specific set of conditions enabled this market for ideas to emerge in Europe in 1500–1700.

* Europe's political fragmentation of city states, duchies, bishoprics creating competition to attract scholars.

* Suppressing new ideas became impossible due to scholars' outside options.

* This market for ideas saw the emergence of the belief in progress.
* Relatively few of these innovations found applications in the early inventions of the Industrial Revolution, especially in the cotton industry.

* But crucial inventions required such innovations, such as hydraulic and steam power.

* Attitudes from the Enlightenment drove the practical innovations later on.

* Formal knowledge increasingly found applications later on, after the 1820s (chemistry, thermodynamics, electricity).
* For economic performance, innovations should be translated to ingenious devices that could be scaled up and repaired by local mechanics.

* It required a large availability of skilled artisans throughout the country, which Britain had (not France).

* Apprenticeship was key, especially a country-wide market for apprentices.
Almelhem, Iyigun, Kennedy, and Rubin (2023)

* How did cultural change impact Britain's industrialization?

* Empirical analysis of progress-oriented view of science promoted by Enlightenment thinkers: a “culture of growth”.
## Almelhem, Iyigun, Kennedy, and Rubin (2023)

* How did cultural change impact Britain's industrialization?

* Empirical analysis of progress-oriented view of science promoted by Enlightenment thinkers: a “culture of growth”.

* Method:

* Textual analysis of 173k volumes published in 1500–1900.

* Unsupervised machine learning to extract 60 latent topics.

* Classify topics into 3 categories: science, religion, political economy.

* Categories reflect the “language” used in these texts, regardless of their content.
Categories

<table><caption>Table 1: Categories</caption><thead><tr><td>Category</td><td>Topics and associated words</td></tr></thead><tbody><tr><td></td><td>33 - law lord show public evid opinion fact</td></tr><tr><td>“Political Economy”</td><td>34 - govern nation polit parliament constitut war parti<br>47 - trade amount labour money price cent increas</td></tr><tr><td>“Religion”</td><td>4 - church christian christ bishop holi paul doctrin<br>12 - god christ lord thi faith holi sin<br>52 - hath fame religion men shew virtu likewis</td></tr><tr><td>“Science”</td><td>7 - fig water iron engin pressur steam electr<br>8 - acid solut heat carbon water sulphur iron<br>41 - line angl equal equat sin sun plane</td></tr></tbody></table>

Source: Almelhem et al. (2023).
* Place individual topics relative to each category.

* Objective: observe how close topics are to one category.

* Each topic gets a yearly score in each category based on topics' placement in 1850.

* Tells about the type of "language" used by a topic, which is classified in a category.
* Place individual topics relative to each category.

* Objective: observe how close topics are to one category.

* Each topic gets a yearly score in each category based on topics' placement in 1850.

* Tells about the type of "language" used by a topic, which is classified in a category.

* The language of science separates from the religion before the IR.
<figure><img src="image_350.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_351.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_352.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_353.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_354.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_355.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_356.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
<figure><img src="image_357.png" /><figcaption>Categorization of topics into categories</figcaption></figure>

Source: Almelhem et al. (2023). Red: political economy topics. Blue: religion topics. Green: science topics.
* Classify each volume in a category (science, religion, political economy).

• Analyze the type of language used in each volume.
* Classify each volume in a category (science, religion, political economy).

• Analyze the type of language used in each volume.

* Science volumes see their use of the language of science increase in early 18th century at the expense of the language of religion.
Relationship between categories over time, within volumes

<figure><img src="image_358.png" /></figure>

Source: Almelhem et al. (2023).
Relationship between categories over time, within volumes

<figure><img src="image_359.png" /></figure>

Source: Almelhem et al. (2023).
Relationship between categories over time, within volumes

<figure><img src="image_360.png" /></figure>

Source: Almelhem et al. (2023).
* Assess if language of science became more “progress oriented”.

* Use a “progress” dictionary of words based on a thesaurus and dictionaries prior to 1643 (Newton’s birth): progress, improvement, stride, betterment, advance, rise, amelioration.

* Calculate a sentiment index for each volume.

* Then, plot the index across categories over time.
<figure><img src="image_361.png" /><figcaption>Average profess score per science volume</figcaption></figure>

Source: Almelhem et al. (2023).
<figure><img src="image_362.png" /><figcaption>Progress sentiment (1550-1850)</figcaption></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
<figure><img src="image_363.png" /><figcaption>Progress sentiment (1550–1850)</figcaption></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
<figure><img src="image_364.png" /><figcaption>Progress sentiment (1550–1850)</figcaption></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
<figure><img src="image_365.png" /><figcaption>Progress sentiment (1550–1850)</figcaption></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
<figure><img src="image_366.png" /><figcaption>Progress sentiment (1550–1850)</figcaption></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
<figure><img src="image_367.png" /><figcaption>Progress sentiment (1550–1850)</figcaption></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
Progress sentiment (1550–1850)

<figure><img src="image_368.png" /></figure>

Source: Almelhem et al. (2023). Lighter colors represent more progressive sentiment
Steinsson (2020), chap 2

* There were always thinkers before.
## Steinsson (2020), chap 2

* There were always thinkers before.

* Diffusion of ideas was limited:

* Without writing, technological regressions if no transmission (e.g., Tasmania).

* When writings rare, subject to destruction (e.g., Alexandria).

* Suppression of knowledge by interest groups (e.g., Church).
# Why did not occur before?

## Steinsson (2020), chap 2

* There were always thinkers before.

* Diffusion of ideas was limited:

* Without writing, technological regressions if no transmission (e.g., Tasmania).

* When writings rare, subject to destruction (e.g., Alexandria).

* Suppression of knowledge by interest groups (e.g., Church).

⇒ The watershed moment: the printing press?
* Movable printing press: Guntenberg 1450s.

* A watershed moment?

* Altered balance between suppression and spread of knowledge.

* Enabled rapid diffusion of knowledge.
* Challenge 1: invented in China in 11th century.

* Chinese characters: thousands of pieces to transport.

* Not as useful invention for diffusion.
* Challenge 1: invented in China in 11th century.

* Chinese characters: thousands of pieces to transport.

* Not as useful invention for diffusion.

* Challenge 2: Industrial Revolutions occurred 300 years later.

* Took time to weaken knowledge suppression forces.

* Reformation (16–17th) precursor to Enlightenment (17–18th).

* Printing press (15th) precursor to Reformation (16–17th).
* Luther against Catholic Church's indulgences (1510s Wittenberg).

* Effective use of printed pamphlet in German to spread ideas.

* Reformation laid ground for Enlightenment:

* Censorship less severe in protestant locations (Britain, NL).

* Reformation encouraged literacy (Bible reading).

* Literacy rose massively before the Industrial Revolution.

* Also a consequence of decline in book prices.
# Literacy rates

<figure><img src="image_369.png" /><figcaption>Literacy rate of husbandmen in London and Middlesex (1560–1740)</figcaption></figure>

Literacy: fraction of witnesses who can sign ecclesiastical records or court depositions

Source: Steinsson (2020) from Cressy (1980) and Houston (1982).
<figure><img src="image_370.png" /><figcaption>Literacy rate in England (1580–1920)</figcaption></figure>

Literacy: fraction of witnesses who can sign ecclesiastical records, court depositions, or marriage registers

Source: Clark (2014) based on Schofield (1973), Houston (1982), Cressy (1977), and Clark (2007).
A Protestant prosperity?
* “Culture of growth” encapsulates complex causal links:

* The printing press

* The Reformation and Protestantism

* Literacy

* The Enlightenment

* The Industrial Revolution
* "Culture of growth" encapsulates complex causal links:

* The printing press

* The Reformation and Protestantism

* Literacy

* The Enlightenment

* The Industrial Revolution

⇒ Let's try to disentangle these empirically.
## Becker and Woessmann (2009)

* "Was Weber wrong? A human capital theory of protestant economic history."
## Becker and Woessmann (2009)

* "Was Weber wrong? A human capital theory of protestant economic history."

* Weber: Protestant ethic and the spirit of capitalism (early 1900s).

* Empirical foundation: Protestant locations more prosperous.

* Protestantism and religious sanctification of labor and saving.

* Resulted in strong work ethic and capitalism.

* Contrast with Catholicism, focused on morality and asceticism.
<figure><img src="image_371.png" /><figcaption>Cross-Country Pattern of Protestantism and GDP per Capita, 1900</figcaption></figure>

Source: Becker and Woessmann (2009).
* Empirical test for Protestantism ⇒ prosperity:

* Correlation: local prevalence of Protestantism and prosperity in 1900?

* But omitted variable problem: other factors may cause both (e.g., literacy).
* Empirical test for Protestantism ☐⇒ prosperity:

* Correlation: local prevalence of Protestantism and prosperity in 1900?

* But omitted variable problem: other factors may cause both (e.g., literacy).

* Need instrumental variable:

* Relevance: correlation with variation in Protestantism.

* Exogeneity: uncorrelated with other determinants of prosperity.

☐⇒ Focus on component of variation in Protestantism uncorrelated with other determinants of prosperity.
<figure><img src="image_372.png" /><figcaption>Cross-Country Pattern of Protestantism and Literacy, 1900</figcaption></figure>

Source: Becker and Woessmann (2009).
# Protestant prosperity: instrument

* Literacy as an instrument?

* Relevance: more literate locations more likely to adopt due to Luther's emphasis on reading Bible in vernacular.

* Exogeneity: literacy affects directly prosperity (information, innovation, contracting,....).
# Protestant prosperity: instrument

* Literacy as an instrument?

* Relevance: more literate locations more likely to adopt due to Luther's emphasis on reading Bible in vernacular.

* Exogeneity: literacy affects directly prosperity (information, innovation, contracting,....).

* Distance to Wittenberg:

* Relevance: more likely to adopt if more exposed to Luther ideas; travel and transmission of information was slow.

⇒ The Reformation diffused from Wittenberg.
# Protestant prosperity: instrument

* Literacy as an instrument?

* Relevance: more literate locations more likely to adopt due to Luther's emphasis on reading Bible in vernacular.

* Exogeneity: literacy affects directly prosperity (information, innovation, contracting,....).

* Distance to Wittenberg:

* Relevance: more likely to adopt if more exposed to Luther ideas; travel and transmission of information was slow.
  ⇒ The Reformation diffused from Wittenberg.

* Exogeneity: before the Reformation, Wittenberg unimportant place.
  ⇒ No direct prosperity effect of being close to Wittenberg.
## Protestant Population Share in Prussia in 1871

<figure><img src="image_373.png" /></figure>

Source: Steinsson (2020) based on Becker and Woessmann (2009).
* Sample: 452 counties of Prussia, dominant German State.

* Treatment: Protestant population share in 1871.

* Instrument: distance to Wittenberg.

* Outcomes:

* Income tax revenue per capita in 1877.

* Annual incomes for elementary school teachers in 1886.

* Share labor force in manufacturing in 1882.

* Balancing variables:

* Urbanization rate in 1500.

* Indicators for imperial city, university, school in 1517.
* Formal test of instrument relevance (first-stage regression):

$$PROT_i = \alpha + \beta DIST_i + \mathbf{X}_i'\gamma + \varepsilon_i$$

* PROTᵢ: population share of Protestants in county i in 1871.

* DISTᵢ: distance to Wittenberg in km.

* Xᵢ: vector of control variables (share younger than 10, share Jews, share female, share born in municipality, household size, population size,...).

* εᵢ: error term.
## First-Stage: Protestantism and Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Share Protestants</th></tr></thead><tbody><tr><td>Distance to Wittenberg in km</td><td>-0.095<br>(0.011)</td></tr><tr><td>Number of Observations</td><td>452</td></tr><tr><td>1st-Stage F-Statistic</td><td>74.2</td></tr></tbody></table>

*Notes:* These estimates are taken from Table III in Becker and Woessmann (2009). Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % Jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % missing education info, % blind, % deaf-mute, % insane.

Source: Steinsson (2020) based on Becker and Woessmann (2009).
## First-Stage: Protestantism and Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Share Protestants</th></tr></thead><tbody><tr><td>Distance to Wittenberg in km</td><td>-0.095<br>(0.011)</td></tr><tr><td>Number of Observations</td><td>452</td></tr><tr><td>1st-Stage F-Statistic</td><td>74.2</td></tr></tbody></table>

*Notes:* These estimates are taken from Table III in Becker and Woessmann (2009). Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % Jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % missing education info, % blind, % deaf-mute, % insane.

Source: Steinsson (2020) based on Becker and Woessmann (2009).

* Each 100km is associated with ↓ Protestant share of 9.5 pp.

* Precise estimate: clearly reject that $\beta = 0$.

* F-statistics > 10 ≈ “strong instrument”.
* Corroboration of instrument exogeneity.

* Instrument not correlated with observable economic characteristics prior to Reformation that may affect subsequent prosperity.

* Find proxies for economic or educational development.
# Protestant prosperity: exogeneity

* Corroboration of instrument exogeneity.

* Instrument not correlated with observable economic characteristics prior to Reformation that may affect subsequent prosperity.

* Find proxies for economic or educational development.

$$Y_i = \alpha + \beta DIST_i + \mathbf{X}'_i \gamma + \varepsilon_i$$

* $Y_i$: Urbanization, imperial city, university, school in 1500s.

* $DIST_i$: distance to Wittenberg in km.

* $\mathbf{X}_i$: vector of control variables (share younger than 10, share Jews, share female, share born in municipality, household size, population size,...).

* $\varepsilon_i$: error term.
## Balance Tests: Pre-Protestant Prosperity and Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Imperial City<br>in 1517</th><th>Urbanization<br>in 1500</th><th>University<br>in 1517</th><th>School<br>in 1517</th></tr></thead><tbody><tr><td>Distance to Wittenberg<br>(in 100 km)</td><td>0.0034<br>(0.0071)</td><td>0.00006<br>(0.00013)</td><td>-0.0019<br>(0.0047)</td><td>-0.0073<br>(0.0099)</td></tr><tr><td>Number of Observations</td><td>452</td><td>452</td><td>452</td><td>333</td></tr><tr><td>R<sup>2</sup></td><td>0.0005</td><td>0.0004</td><td>0.0004</td><td>0.002</td></tr></tbody></table>

*Notes:* These estimates are taken from Table IV in Becker and Woessmann (2009). Standard errors are in parentheses. The dependent variables are: an indicator for whether a county was a free imperial city, urban population per km², indicator for whether county had a university, and indicator for whether the county had a school.

Source: Steinsson (2020) based on Becker and Woessmann (2009).
## Balance Tests: Pre-Protestant Prosperity and Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Imperial City<br>in 1517</th><th>Urbanization<br>in 1500</th><th>University<br>in 1517</th><th>School<br>in 1517</th></tr></thead><tbody><tr><td>Distance to Wittenberg<br>(in 100 km)</td><td>0.0034<br>(0.0071)</td><td>0.00006<br>(0.00013)</td><td>-0.0019<br>(0.0047)</td><td>-0.0073<br>(0.0099)</td></tr><tr><td>Number of Observations</td><td>452</td><td>452</td><td>452</td><td>333</td></tr><tr><td>R<sup>2</sup></td><td>0.0005</td><td>0.0004</td><td>0.0004</td><td>0.002</td></tr></tbody></table>

*Notes:* These estimates are taken from Table IV in Becker and Woessmann (2009). Standard errors are in parentheses. The dependent variables are: an indicator for whether a county was a free imperial city, urban population per km², indicator for whether county had a university, and indicator for whether the county had a school.

Source: Steinsson (2020) based on Becker and Woessmann (2009).

* Coefficients are small in magnitude.

* Imprecise estimate: fail to reject that $\beta = 0$.

* Claim: distance to Wittenberg is valid instrument.
* Is instrument correlated with outcome (reduced-form)?

* Is distance to Wittenberg correlated with prosperity in 1870s?

$$ \text{PROSP}_i = \alpha + \beta\text{DIST}_i + \mathbf{X}'_i\gamma + \varepsilon_i $$

* $\text{PROT}_i$: proxy for prosperity of county $i$ in 1870s.

* $\text{DIST}_i$: distance to Wittenberg in km.

* $\mathbf{X}_i$: vector of control variables (share younger than 10, share Jews, share female, share born in municipality, household size, population size,...).

* $\varepsilon_i$: error term.
## Reduced-Form: Prosperity and Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Income Tax per capita</th><th>Log Teacher Income</th><th>Share Manuf. and Services</th></tr></thead><tbody><tr><td>Distance to Wittenberg (in 100 km)</td><td>-6.0<br>(2.3)</td><td>-1.00<br>(0.48)</td><td>-0.78<br>(0.36)</td></tr><tr><td>Number of Observations</td><td>426</td><td>452</td><td>452</td></tr></tbody></table>

Notes: These estimates are produced using replication code and data provided by Ludger Woessmann. They are the reduced form estimates that correspond to the instrumental variables (IV) estimates presented in Table V of Becker and Woessmann (2009) except that I have included “% missing eduction info” as an additional control for consistency with the first stage reported in Table 2. Income tax per capita is measured in pfennig. Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % missing eduction info, % blind, % deaf-mute, % insane.

Source: Steinsson (2020) based on Becker and Woessmann (2009).
## Reduced-Form: Prosperity and Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Income Tax per capita</th><th>Log Teacher Income</th><th>Share Manuf. and Services</th></tr></thead><tbody><tr><td>Distance to Wittenberg (in 100 km)</td><td>-6.0 (2.3)</td><td>-1.00 (0.48)</td><td>-0.78 (0.36)</td></tr><tr><td>Number of Observations</td><td>426</td><td>452</td><td>452</td></tr></tbody></table>

Notes: These estimates are produced using replication code and data provided by Ludger Woessmann. They are the reduced form estimates that correspond to the instrumental variables (IV) estimates presented in Table V of Becker and Woessmann (2009) except that I have included “% missing education info” as an additional control for consistency with the first stage reported in Table 2. Income tax per capita is measured in pfennig. Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % missing education info, % blind, % deaf-mute, % insane.

Source: Steinsson (2020) based on Becker and Woessmann (2009).

* Each 100km is associated with ↓ income tax revenues of 6 pfennig.

* Precise estimate: clearly reject that $β = 0$.

* Magnitude:

* s.d. in DIST = 150km. s.d. in INC = 70 pfennig.

* ↑ 1 s.d. DIST ⇒ ↓ 0.13 s.d. INC (small-moderate).
* Second-stage (IV): impact of Protestantism on prosperity through distance to Wittenberg.

* If valid: IV not correlated with all other determinants of prosperity.

* As move away from Wittenberg, the only potential determinant of prosperity changing is share Protestantism.

* If prosperity changes as move away from Wittenberg, only due to changes in Protestantism.
* Causal chain:

$$ \text{DIST on PROSP} = \text{DIST on PROT} \times \text{PROT on PROSP} $$
* Causal chain:

$$ \text{DIST on PROSP} = \text{DIST on PROT} \times \text{PROT on PROSP} $$

* Second-stage is ratio of reduced-form to first-stage:

$$ \text{PROT on PROSP} = \frac{\text{DIST on PROT}}{\text{DIST on PROSP}} $$
## Second-Stage: Prosperity and Protestantism Through Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Income Tax per capita</th><th>Log Teacher Income</th><th>Share Manuf. and Services</th></tr></thead><tbody><tr><td>Share Protestant</td><td>0.62<br>(0.24)</td><td>0.11<br>(0.05)</td><td>0.08<br>(0.04)</td></tr><tr><td>Number of Observations</td><td>426</td><td>452</td><td>452</td></tr></tbody></table>

*Notes:* These estimates are taken from Table V in Becker and Woessmann (2009). Income tax per capita is measured in pfennig. Coefficients in the second column are multiplied by 100. Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % Jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % blind, % deaf-mute, % insane.

Source: Steinsson (2020) based on Becker and Woessmann (2009).
# Protestant prosperity: second-stage

## Second-Stage: Prosperity and Protestantism Through Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Income Tax per capita</th><th>Log Teacher Income</th><th>Share Manuf. and Services</th></tr></thead><tbody><tr><td>Share Protestant</td><td>0.62<br>(0.24)</td><td>0.11<br>(0.05)</td><td>0.08<br>(0.04)</td></tr><tr><td>Number of Observations</td><td>426</td><td>452</td><td>452</td></tr></tbody></table>

Notes: These estimates are taken from Table V in Becker and Woessmann (2009). Income tax per capita is measured in pfennig. Coefficients in the second column are multiplied by 100. Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % Jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % blind, % deaf-mute, % insane.

Source: Steinson (2020) based on Becker and Woessmann (2009).

* ↑ 10 pp share Protestant = ↑ income tax revenues of 6.2 pfennig.

* Relatively precise estimate: reject that $\beta = 0$.

* Magnitude:

* s.d. in PROT = 37%. s.d. in INC = 70 pfennig.

* ↑ 1 s.d. PROT = ↓ 0.33 s.d. INC (moderate).
# Protestant prosperity: mechanisms

* Was Weber right?

* Was it Protestant ethic and the spirit of capitalism?
# Protestant prosperity: mechanisms

* Was Weber right?

* Was it Protestant ethic and the spirit of capitalism?

* Becker and Woessmann claim: through literacy.

* Effect of Protestantism on literacy large.

* Enough to explain effect of Protestantism on prosperity.
## Mechanisms: Literacy and Protestantism Through Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Share<br>Literate</th></tr></thead><tbody><tr><td>Share Protestant</td><td>0.19<br>(0.03)</td></tr><tr><td>Number of Observations</td><td>452</td></tr></tbody></table>

*Notes:* This IV estimate is taken from Table III in Becker and Woessmann (2009). Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % Jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % blind, % deaf-mute, % insane, % missing education info.

Source: Steinsson (2020) based on Becker and Woessmann (2009).
## Mechanisms: Literacy and Protestantism Through Distance to Wittenberg

<table><thead><tr><th>Dependent Variable:</th><th>Share<br>Literate</th></tr></thead><tbody><tr><td>Share Protestant</td><td>0.19<br>(0.03)</td></tr><tr><td>Number of Observations</td><td>452</td></tr></tbody></table>

*Notes:* This IV estimate is taken from Table III in Becker and Woessmann (2009). Standard errors are in parentheses. The control variables included in the regression are: % age below 10, % Jews, % females, % born in municipality, % of Prussian origin, average household size, ln(population size), population growth from 1867-1871 in %, % blind, % deaf-mute, % insane, % missing education info.

Source: Steinsson (2020) based on Becker and Woessmann (2009).

* ↑ 10 pp share Protestant = ⇒ ↑ literacy of 2 pp.

* Relatively precise estimate: reject that $\beta = 0$.
* Claim: Protestantism ⇌ literacy ⇌ prosperity.
* Claim: Protestantism ⇒ literacy ⇒ prosperity.

* Empirical evidence:

* Based on local differences in Protestantism and prosperity.

* Unclear what are aggregate effects of Protestantism.
* Validity of instrument:

* Evidence of printing press = Protestantism. Rubin (2014)

* Evidence of printing press = prosperity *directly*. Dittmar (2011)
    = Threat to exogeneity of IV

* Protestantism correlated with printing press, no effect on prosperity?
* Validity of instrument:

* Evidence of printing press = Protestantism. Rubin (2014)

* Evidence of printing press = prosperity *directly*. Dittmar (2011)
    = Threat to exogeneity of IV

* Protestantism correlated with printing press, no effect on prosperity?

* Controlling for distance to Mainz (printing press diffusion). Steinsson (2020)

* First stage unchanged (DIST on PROT | PRINT).

* IV weak and insignificant (PROT on PROSP through DIST | PRINT).

= Challenging to disentangle Protestantism, literacy, and growth!
# Protestant prosperity: mechanisms

The diffusion of the movable type printing press

a: Cities with Printing in 1450

<figure><img src="image_374.png" /></figure>

b: Cities with Printing in 1460

<figure><img src="image_375.png" /></figure>

Source: Dittmar (2011).
# Protestant prosperity: mechanisms

The diffusion of the movable type printing press

c: Cities with Printing in 1470

<figure><img src="image_376.png" /></figure>

d: Cities with Printing in 1480

<figure><img src="image_377.png" /></figure>

Source: Dittmar (2011).
# Protestant prosperity: mechanisms

The diffusion of the movable type printing press

c: Cities with Printing in 1490

<figure><img src="image_378.png" /></figure>

f: Cities with Printing in 1500

<figure><img src="image_379.png" /></figure>

Source: Dittmar (2011).
Distance from Mainz and adoption of the printing press, 1450–1500

<figure><img src="image_380.png" /></figure>

Source: Dittmar (2011).
Kersting et al. (2021)

* The role of savings ⇒ accumulation of capital.

* The role of differences between ethnic groups as confounding factor.
* Sample: 434 counties of Prussia, dominant German State.

* Treatment: Protestant population share.

* Instrument: Protestant population share in 1624, residualized.

* Outcomes Lehmann-Hasemeyer and Wahl (2017):

* Savings per capita (medium and lower income groups).

* Number of saving banks.

* Years ≈ 1875, 1880, 1890, 1900, 1905.
<figure><img src="image_381.png" /><figcaption>Savings per Capita, 1880</figcaption></figure>

Source: Kersting et al (2020).
<figure><img src="image_382.png" /><figcaption>Savings per Capita, 1905</figcaption></figure>

Source: Kersting et al (2020).
Protestantism, 1900

<figure><img src="image_383.png" /></figure>

Source: Kersting et al (2020).
* Baseline pooled OLS regression:

$$ Sav_{it} = \alpha_1 + \beta_1 Prot_{it} + \mathbf{X}'_{it} \gamma_1 + \varepsilon_{1it} $$

* Sav$_{it}$: savings per capita in county i in year t.

* Prot$_{it}$: share of Protestants in county i.

* X$_{it}$: vector of control variables (urban population, number of savings banks, household size, share of other religions).

* ε$_{it}$: error term.
* Idiosyncratic element of religious denomination as of 1624 (Peace of Westphalia). Spenkuch (2017)

* Regress Protestant 1624 at county level on predictors:

* Cantoni (2012) Latitude, contribution to federal budget of Holy Roman Empire, distance to Wittenberg.

* Rubin (2014) Existence of printing press.

* Use residuals as IV.

* No reverse causality (200-year lag).

* By construction, uncorrelated with factors affected by controls.
Savings per capita, 1875–1905

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel 1: OLS</strong></td></tr><tr><td></td><td></td><td colspan="3">Dep. Var. Savings per Capita</td></tr><tr><td>Share Protestants</td><td>0.012<br>(0.079)</td><td>0.022<br>(0.102)</td><td>0.033<br>(0.081)</td><td>0.065<br>(0.103)</td></tr><tr><td>Income per capita</td><td></td><td></td><td>0.350***<br>(0.068)</td><td>0.316***<br>(0.072)</td></tr><tr><td><i>R</i><sup>2</sup></td><td>0.280</td><td>0.245</td><td>0.351</td><td>0.308</td></tr><tr><td colspan="5"><strong>Panel 2: Second Stage</strong></td></tr><tr><td></td><td></td><td colspan="3">Dep. Var. Savings per Capita</td></tr><tr><td>Share Protestants</td><td>-0.034<br>(0.101)</td><td>-0.029<br>(0.183)</td><td>-0.057<br>(0.110)</td><td>-0.064<br>(0.193)</td></tr><tr><td>Income per capita</td><td></td><td></td><td>0.342***<br>(0.072)</td><td>0.297**<br>(0.084)</td></tr><tr><td colspan="5"><strong>Panel 3: First Stage</strong></td></tr><tr><td></td><td></td><td colspan="3">Dep. Var. Protestantism</td></tr><tr><td>Residual decision 1624</td><td>0.429***<br>(0.083)</td><td>0.288***<br>(0.048)</td><td>0.432***<br>(0.081)</td><td>0.294***<br>(0.045)</td></tr><tr><td>Income per capita</td><td></td><td></td><td>-0.109<br>(0.092)</td><td>-0.162*<br>(0.080)</td></tr><tr><td><i>R</i><sup>2</sup></td><td>0.324</td><td>0.392</td><td>0.331</td><td>0.409</td></tr><tr><td>Including eastern provinces</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Further controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>F-statistic excluded instruments</td><td>26.65</td><td>36.26</td><td>28.73</td><td>42.32</td></tr><tr><td>N</td><td>1.830</td><td>1.355</td><td>1.830</td><td>1.355</td></tr></tbody></table>
Effect of Protestantism on literacy, 1871

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td colspan="4"><strong>Panel 1: OLS</strong></td></tr><tr><td></td><td colspan="3">Dep. Var. Literacy</td></tr><tr><td>Share Protestants</td><td>0.057***<br>(0.009)</td><td>0.033**<br>(0.013)</td><td>0.040***<br>(0.012)</td></tr><tr><td>Share German speaking</td><td></td><td></td><td>0.213***<br>(0.036)</td></tr><tr><td><i>R</i><sup>2</sup></td><td>0.610</td><td>0.431</td><td>0.735</td></tr><tr><td colspan="4"><strong>Panel 2: Second Stage</strong></td></tr><tr><td></td><td colspan="3">Dep. Var. Literacy</td></tr><tr><td>Share Protestants</td><td>0.030<br>(0.018)</td><td>0.031<br>(0.017)</td><td>0.020<br>(0.015)</td></tr><tr><td>Share German speaking</td><td></td><td></td><td>0.221***<br>(0.036)</td></tr><tr><td colspan="4"><strong>Panel 3: First Stage</strong></td></tr><tr><td></td><td colspan="3">Dep. Var. Protestantism</td></tr><tr><td>Residual decision 1624</td><td>46.088***<br>(4.227)</td><td>43.094***<br>(3.277)</td><td>45.369***<br>(2.866)</td></tr><tr><td>Share German speaking</td><td></td><td></td><td>0.327**<br>(0.144)</td></tr><tr><td><i>R</i><sup>2</sup></td><td>0.474</td><td>0.599</td><td>0.489</td></tr><tr><td>Including eastern provinces</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Further controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>F-statistic excluded instruments</td><td>246.34</td><td>172.95</td><td>250.58</td></tr><tr><td>N</td><td>378</td><td>280</td><td>378</td></tr></tbody></table>
Geography

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_384.png" /></figure>

<figure><img src="image_385.png" /></figure>

<figure><img src="image_386.png" /></figure>

Toulouse
School of
Economics
Can geography explain the Great Divergence?
* Geography may matter through several channels:

* Efforts and attitudes (Montesquieu). Not credible.

* The timing of the Neolithic transition.

* Territorial fragmentation and state building.

* Natural endowments.
# The timing of the Neolithic Transition

Spolaore and Wacziarg (2013)

* Prehistoric and biological conditions ⇒ spread of agriculture and domestication. Diamond (1997) Olsson and Hibbs (2005)

* Initial advantages of Eurasia:

* Initial biological conditions (diversity of animals and plants).

* East-West orientation facilitating spread of agricultural innovation.
    ⇒ Population growth and early Neolithic transition.

* Lead from transition enabled Europeans to dominate other regions.

* Empirical evidence for correlation between biogeographic endowments and long-run development.
# The timing of the Neolithic Transition

## GEOGRAPHY AND CONTEMPORARY DEVELOPMENT

(Dependent variable: log per capita income, 2005; estimator: OLS)

<table><thead><tr><th rowspan="2">Sample:</th><th>Whole World</th><th>Olsson-Hibbs sample<sup>a</sup></th><th>Olsson-Hibbs sample<sup>a</sup></th><th>Olsson-Hibbs sample<sup>a</sup></th><th>Olsson-Hibbs sample<sup>a</sup></th><th>Old World only</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Absolute latitude</td><td>0.044<br>(6.645)***</td><td>0.052<br>(7.524)***</td><td></td><td></td><td></td><td></td></tr><tr><td>Percent land area in the tropics</td><td>-0.049<br>(0.154)</td><td>0.209<br>(0.660)</td><td>-0.410<br>(1.595)</td><td>-0.650<br>(2.252)**</td><td>-0.421<br>(1.641)</td><td>-0.448<br>(1.646)</td></tr><tr><td>Landlocked dummy</td><td>-0.742<br>(4.375)***</td><td>-0.518<br>(2.687)***</td><td>-0.499<br>(2.487)**</td><td>-0.572<br>(2.622)**</td><td>-0.505<br>(2.523)**</td><td>-0.226<br>(1.160)</td></tr><tr><td>Island dummy</td><td>0.643<br>(2.496)**</td><td>0.306<br>(1.033)</td><td>0.920<br>(3.479)***</td><td>0.560<br>(1.996)**</td><td>0.952<br>(3.425)***</td><td>1.306<br>(4.504)***</td></tr><tr><td>Geographic conditions (Olsson-Hibbs)<sup>b</sup></td><td></td><td></td><td>0.706<br>(6.931)***</td><td></td><td>0.768<br>(4.739)***</td><td>0.780<br>(5.167)***</td></tr><tr><td>Biological conditions (Olsson-Hibbs)<sup>c</sup></td><td></td><td></td><td></td><td>0.585<br>(4.750)***</td><td>-0.074<br>(0.483)</td><td>0.086<br>(0.581)</td></tr><tr><td>Constant</td><td>7.703<br>(25.377)***</td><td>7.354<br>(25.360)***</td><td>8.745<br>(61.561)***</td><td>8.958<br>(58.200)***</td><td>8.741<br>(61.352)***</td><td>8.438<br>(60.049)***</td></tr><tr><td>Observations</td><td>155</td><td>102</td><td>102</td><td>102</td><td>102</td><td>83</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.440</td><td>0.546</td><td>0.521</td><td>0.449</td><td>0.516</td><td>0.641</td></tr></tbody></table>

### Notes:

<sup>a</sup>The Olsson and Hibbs sample excludes the neo-European countries (Australia, Canada, New Zealand, and the United States) and countries whose current income is based primarily on extractive wealth (Olsson and Hibbs 2005).

<sup>b</sup>First principal component of number of annual or perennial wild grasses and number of domesticable big mammals (all variables from Olsson and Hibbs 2005).

<sup>c</sup>First principal component of absolute latitude; climate suitability to agriculture; rate of East-West orientation; size of landmass in millions of sq km (all variables from Olsson and Hibbs 2005).
# The timing of the Neolithic Transition

* Geographic factors influenced timing of adoption of agriculture
  Ashraf and Galor (2011)

* Quantify impact of Neolithic transition for development in 1500.

* First stage: geography ⇒ timing of transition.

* Reduced form: geography ⇒ population density 1500.

* Second stage: timing of transition (IV: geography) ⇒ population density 1500 ($\hat{\beta}_0$ = 0.88).

* Exclusion restriction: geography only affects population density through Neolithic transition.

* Biogeographic factors matter through early exposure to agriculture.
# The timing of the Neolithic Transition

## GEOGRAPHY AND DEVELOPMENT IN 1500 AD

<table><thead><tr><th rowspan="2">Dependent Variable:</th><th colspan="4">Years since agricultural transition</th></tr><tr><th>OLS</th><th>OLS</th><th>OLS</th><th>IV</th></tr></thead><tbody><tr><td>Estimator:</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>Absolute latitude</td><td>-0.074<br>(3.637)***</td><td>-0.022<br>(1.411)</td><td>0.027<br>(2.373)**</td><td>0.020<br>(1.872)*</td></tr><tr><td>Percent land area in the tropics</td><td>-1.052<br>(2.356)**</td><td>0.997<br>(2.291)**</td><td>1.464<br>(3.312)***</td><td>1.636<br>(3.789)***</td></tr><tr><td>Landlocked dummy</td><td>-0.585<br>(2.306)**</td><td>0.384<br>(1.332)</td><td>0.532<br>(1.616)</td><td>0.702<br>(2.158)**</td></tr><tr><td>Island dummy</td><td>-1.085<br>(3.699)***</td><td>0.072<br>(0.188)</td><td>0.391<br>(0.993)</td><td>0.508<br>(1.254)</td></tr><tr><td>Number of annual or<br>perennial wild grasses</td><td>0.017<br>(0.642)</td><td>0.030<br>(1.105)</td><td></td><td></td></tr><tr><td>Number of domesticable<br>big mammals</td><td>0.554<br>(8.349)***</td><td>0.258<br>(3.129)***</td><td></td><td></td></tr><tr><td>Years since agricultural transition</td><td></td><td></td><td>0.426<br>(6.694)***</td><td>0.584<br>(6.887)***</td></tr><tr><td>Constant</td><td>4.657<br>(9.069)***</td><td>-0.164<br>(0.379)</td><td>-2.159<br>(4.421)***</td><td>-2.814<br>(5.463)***</td></tr><tr><td>Observations</td><td>100</td><td>100</td><td>98</td><td>98</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.707</td><td>0.439</td><td>0.393</td><td>—</td></tr></tbody></table>
Fernandez-Villaverde, Koyama, Lin, and Sng (2023)

* Great Divergence ⇐ Inclusive institutions ⇐ Early state formation
  ⇐ Inter-state competition ⇐ Political fragmentation in Europe

* Source of political fragmentation: fractured land? Diamond (1997)

* Fractured = mountains, forests, ruggedness.

* Impedes development of large empires in Europe vs China.

* But China more mountainous than Europe. Hoffman (2015)

* Too deterministic explanation?

⇒ Empirical examination
* Long-standing argument:

* States need large area of productive land for rents.

* Agricultural output needs easy appropriation.

* Easiness of appropriation depends on geography.
* Long-standing argument:

* States need large area of productive land for rents.

* Agricultural output needs easy appropriation.

* Easiness of appropriation depends on geography.

* Concept of geographical core:

* Successful states: core based on self-contained geographical region with fertile land, good transport connections, defensible to invasions.

* Europe did not possess such a core (fractured land).

* China possessed such a core (unified land–though more montaneous).
## Ruggedness in Europe and China proper

<figure><img src="image_387.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
* Europe's cores:

* British Isles

* Scandinavia

* Iberian peninsula

* Italian peninsula

* Northern European plain (Poland, Germany, France)

* China's cores:

* Central-North plain

* Yunnan-Guizhou Plateau in South

* Easy connections with river system
<figure><img src="image_388.png" /><figcaption>## Study area</figcaption></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
* Divide space of study:

* 20,637 hexagonal cells of 28km radius.

* Each cell can sustain a polity.

* Distance by foot in one day: base polity can monitor.

* Area of study:

* "China" 1,434 cells.

* (Western) "Europe" 1,307
* Geographical features affecting likelihood of regional clustering of cells into empires:

* $x_{\text{rugged}}$: ruggedness (average s.d. of elevation)

* $x_{\text{sea}}$: indicator for water as barrier.

* $x_{\text{frigid}}$: indicator for temperature < 0 for > 6 months / year.

* $x_{\text{torrid}}$: indicator for tropical or torrid zone.

* $x_{\text{forest}}$: indicator for ancient forest in 0 CE.

$$ \Rightarrow \mathbf{x} = \{x_{\text{rugged}}, x_{\text{sea}}, x_{\text{frigid}}, x_{\text{torrid}}, x_{\text{forest}}\} $$

* Resource availability: population density in 0 CE (Malthusian logic).
Ruggedness (standard deviation of elevation)

<figure><img src="image_389.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
<figure><img src="image_390.png" /><figcaption>## Auxiliary barriers to conquest</figcaption></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
Population density (0 CE)

<figure><img src="image_391.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
* Time:

* Discrete time $t = 0, 1, 2, ...$

* At $t = 0$, each cell is independent polity.

* Over time, polities expand over cells or lose control of cells.
Cell *k* and adjacent cells

<figure><img src="image_392.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
* **Conquest:**

* In each period, $k$ is in conflict with border cell with probability $\alpha \cdot y_k$, where $y_k$ is productivity (population density) of cell $k$.

* Victory in war depends on aggregate productivity and geographical characteristics:

* More productive win more often.

* Geography makes it harder or easier.

* Resources allocated improve win probability (potentially multiple conflicts).

* Winner absorbs losing polities (single or block).

* Border cells might secede with positive probability.

⇒ Simulate evolution of polities from Iron to Exploration Age
Period 50 (1100 BCE –850 BCE)

<figure><img src="image_393.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
Period 300 (1100 BCE –400 CE)

<figure><img src="image_394.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
Period 500 (1100 BCE –1400 CE)

<figure><img src="image_395.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
Herfindahl indices of political unity: China

<figure><img src="image_396.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
Herfindahl indices of political unity: Europe

<figure><img src="image_397.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2023)
* Despite ruggedness, salient role of North China in fostering unification.

* Flatness of North plain facilitates military conquest.

* Geographical characteristics enable a single powerful state to overcome rivals and build a centralized state.
* Rome dominated early.

* Dense European forest impeded consolidation.

* Mountain barriers raised cost of military intervention in Western Europe.

* Central Habsburg hegemony due to marriages strategy rather than geography.
* Model does not include strategic interactions e.g. investment in military capacity, alliances, etc.

* But would strengthen results through balance of power in Europe.
* Model does not include strategic interactions e.g. investment in military capacity, alliances, etc.

* But would strengthen results through balance of power in Europe.

* Abstract from feedback from economic growth.

⇒ Fractured land generates unification in China and persistent fragmentation in Europe.
* Two types of natural endowments:

* The disease environment and life expectancy.

* Natural resources, e.g., coal and the Industrial Revolution (skip).
* AJR (2001) Colonial origins: settler mortality as IV.
* AJR (2001) Colonial origins: settler mortality as IV.

* Evidence for direct role of disease environment? Sachs (2003)
* AJR (2001) Colonial origins: settler mortality as IV.

* Evidence for direct role of disease environment? Sachs (2003)

* Exclusion restriction: settler mortality affects only current income through historic effect on institutions.

* But disease environment affecting settlers persists, affecting income today.

⇒ Exclusion restriction invalid?
* AJR (2001) Colonial origins: settler mortality as IV.

* Evidence for direct role of disease environment? Sachs (2003)

* Exclusion restriction: settler mortality affects only current income through historic effect on institutions.

* But disease environment affecting settlers persists, affecting income today.

⇒ Exclusion restriction invalid?

* AJR (2001) Malaria and yellow fever affected settlers, natives immune.
* Geography ⇒ disease environment ⇒ health ⇒ economic development?

* Improving health (e.g. malaria) should have great economic returns.
* Geography ⇒ disease environment ⇒ health ⇒ economic development?

* Improving health (e.g. malaria) should have great economic returns.

* Weil (2007) Health capital.

$$F(K, H, Q, A)$$

* K: physical capital.

* A: technology.

* H: human capital.

* Q: health capital.
<figure><img src="image_398.png" /><figcaption>GDP per Worker and Adult Survival Rate</figcaption></figure>

Source: Weil (2007)
* Effect of life expectancy on economic growth. AJ (2007)
* Effect of life expectancy on economic growth. AJ (2007)

$$y_{it} = \pi x_{it} + \zeta_i + \mu_t + Z'_{it}\beta + \varepsilon_{it}$$

* y: log income per capita.

* x: log life expectancy at birth.

* ζ: country FE.

* μt: year FE.
* But yearly changes in life expectancy have full effects in long run.

* Estimate long differences: panel including two dates.

$$ \Delta y_i = \pi \Delta x_i + \Delta \mu + \Delta Z'_i \beta + \Delta \varepsilon_i $$

* $t_0 = 1940$ or $t_0 = 1960$ and $t_1 = 1980$ or $t_1 = 2000$.
Life expectancy and economic growth

* Data:

* Life expectancy, demographic data. UN

* GDP per capita. Maddison
* Data:

* Life expectancy, demographic data. UN

* GDP per capita. Maddison

* Samples:

* Whole world: 120 countries.

* Base sample: 47–59 countries (reliable data).
Life expectancy and economic growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Log Population</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Log life expectancy</td><td>1.60***<br>[0.30]</td><td>1.75***<br>[0.40]</td><td>2.01***<br>[0.22]</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Years</td><td>1960–2000</td><td>1960–2000</td><td>1940–2000</td></tr><tr><td>Sample</td><td>World</td><td>Base</td><td>Base</td></tr><tr><td>Countries</td><td>120</td><td>59</td><td>47</td></tr></tbody></table>
* ↑ 1% in life expectancy ⇒ ↑ 1.6-1.7% in population.

* Not driven by sample selection.
* ↑ 1% in life expectancy ⇒ ↑ 1.6-1.7% in population.

* Not driven by sample selection.

* ↑ 1% in life expectancy ⇒ ↑ 2-3% in total births.

* Also increases ratio of population under age of 20.
Life expectancy and economic growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Log GDP</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Log life expectancy</td><td>1.17***<br>[0.56]</td><td>1.55***<br>[0.35]</td><td>0.85***<br>[0.28]</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Years</td><td>1960–2000</td><td>1960–2000</td><td>1940–2000</td></tr><tr><td>Sample</td><td>World</td><td>Base</td><td>Base</td></tr><tr><td>Countries</td><td>120</td><td>59</td><td>47</td></tr></tbody></table>
Life expectancy and economic growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Log GDP per Capita</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Log life expectancy</td><td>-0.42<br>[0.58]</td><td>-0.19<br>[0.54]</td><td>-1.14***<br>[0.27]</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Years</td><td>1960–2000</td><td>1960–2000</td><td>1940–2000</td></tr><tr><td>Sample</td><td>World</td><td>Base</td><td>Base</td></tr><tr><td>Countries</td><td>120</td><td>59</td><td>47</td></tr></tbody></table>
* ↑ 1% in life expectancy ⇒ ↑ 1.5% in GDP 1960–2000.

* ↑ 1% in life expectancy ⇒ ↑ 0.8% in GDP 1940–1980.

* Not driven by sample selection.
* ↑ 1% in life expectancy ⇒ ↑ 1.5% in GDP 1960–2000.

* ↑ 1% in life expectancy ⇒ ↑ 0.8% in GDP 1940–1980.

* Not driven by sample selection.

* Seems insufficient to compensate for increased population.
* Strong correlation between health and development.

* Causal relationship? OVB and reverse causality
* Strong correlation between health and development.

* Causal relationship? OVB and reverse causality

* ↑ growth rates + ↑ investment in health.

* Better at solving health problems.
* Exploit the "international epidemiological transition" in 1940s–1950s.

* Wave of drug and chemical innovations ⇒ cures for malaria, yellow fever, cholera, smallpox...

* Establishment of World Health Organization ⇒ spread of technology to poorer countries.

⇒ Dramatic improvement of life expectancy across world.
<figure><img src="image_399.png" /><figcaption>Log life expectancy at birth</figcaption></figure>

Source: Acemoglu and Johnson (2007)
* Build instrument for changes in life expectancy:

* Predicted mortality of 15 diseases before intervention.

* Interaction with global intervention dates for each disease.
* Build instrument for changes in life expectancy:

* Predicted mortality of 15 diseases before intervention.

* Interaction with global intervention dates for each disease.

$$M_{it}^{I} = \sum_{d \in D} [(1 - I_{dt}) M_{di40} + I_{dt} M_{dFt}]$$

* $M_{dit}$: mortality in country i from disease d at t.

* $I_{dt}$: indicator for intervention on disease d at t (1 hereafter).

* $D$: set of 15 diseases.
* Build instrument for changes in life expectancy:

* Predicted mortality of 15 diseases before intervention.

* Interaction with global intervention dates for each disease.

$$M'_{it} = \sum_{d \in D} [(1 - I_{dt}) M_{di40} + I_{dt} M_{dFt}]$$

* $M_{dit}$: mortality in country i from disease d at t.

* $I_{dt}$: indicator for intervention on disease d at t (1 hereafter).

* $D$: set of 15 diseases.

* $M_{di40}$: preintervention mortality.

* $M_{dFt}$: mortality from disease d at health frontier at t.

⇒ $M'_{it}$ is an instrument for life expectancy
* Predicted mortality $M'_{it}$ is an instrument for life expectancy

* Effect of global interventions on life expectancy depends on baseline distribution of diseases.

* Only source of variation: interaction baseline distribution with timing of global intervention.

* $I_{dt}$ turns on for all countries at same time.

* No reason for correlation with economic or population changes in given country.

* **Threat:**

* Baseline mortality rates $M'_{d40s}$ predict future changes in population or income.

* Include differential trends to control for this.
First Stage

<figure><img src="image_400.png" /></figure>

Source: Acemoglu and Johnson (2007)
<figure><img src="image_401.png" /><figcaption>First Stage: No Pre-Trends</figcaption></figure>

Source: Acemoglu and Johnson (2007)
<figure><img src="image_402.png" /><figcaption>Reduced Form: Log Population</figcaption></figure>

Source: Acemoglu and Johnson (2007)
<figure><img src="image_403.png" /><figcaption>Reduced Form: Log Total GDP</figcaption></figure>

Source: Acemoglu and Johnson (2007)
Life expectancy and economic growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Log GDP</th><th colspan="2">Log GDP per C</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Log life expectancy</td><td>0.85***<br>[0.28]</td><td>0.42<br>[0.52]</td><td>-1.14***<br>[0.27]</td><td>-1.51***<br>[0.57]</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Estimate</td><td>OLS</td><td>IV</td><td>OLS</td><td>IV</td></tr><tr><td>Years</td><td>1940–2000</td><td>1940–2000</td><td>1940–2000</td><td>1940–2000</td></tr><tr><td>Countries</td><td>47</td><td>47</td><td>47</td><td>47</td></tr></tbody></table>
* Increases in life expectancy increased population and births.

* Source of variation: exogenous differential changes in mortality due to international epidemiological transition.

* Increased population somewhat increased GDP.

* But not enough to offset population growth.
* Increases in life expectancy increased population and births.

* Source of variation: exogenous differential changes in mortality due to international epidemiological transition.

* Increased population somewhat increased GDP.

* But not enough to offset population growth.

⇒ Little direct effect of geography through disease burden.
* Increases in life expectancy increased population and births.

* Source of variation: exogenous differential changes in mortality due to international epidemiological transition.

* Increased population somewhat increased GDP.

* But not enough to offset population growth.

⇒ Little direct effect of geography through disease burden.

* Generated heated debates. Blum et al. (2014) AJ (2014)
* Increases in life expectancy increased population and births.

* Source of variation: exogenous differential changes in mortality due to international epidemiological transition.

* Increased population somewhat increased GDP.

* But not enough to offset population growth.
  ⇒ Little direct effect of geography through disease burden.

* Generated heated debates. Blum et al. (2014) AJ (2014)

* Indirect effect of disease burden through institutions? Alsan (2015)
* Critique of Bloom et al. (2014)

* Healthier countries in 1940 experienced faster subsequent growth and slower health gains.

⇒ Initial life expectancy in 1940 affects subsequent growth.

* The instrument essentially measures national health in 1940.

* Including initial life expectancy is important since strong dependency over time.

⇒ FS vanishes once initial life expectancy vanishes.
<figure><img src="image_404.png" /><figcaption>Log life expectancy in 1914 and predicted mortality</figcaption></figure>

Source: Bloom et al. (2014)
Life expectancy and economic growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Log GDP per Capita</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Log life expectancy (Δ)</td><td>-1.14***<br>[0.27]</td><td>-1.51***<br>[0.57]</td><td>3.68***<br>[1.30]</td><td>-21.56<br>[81.29]</td></tr><tr><td>Log life expectancy (1940)</td><td></td><td></td><td>3.77***<br>[0.94]</td><td>-15.23<br>[61.37]</td></tr><tr><td>Country & year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Estimate</td><td>OLS</td><td>IV</td><td>OLS</td><td>IV</td></tr><tr><td>Years</td><td>1940–2000</td><td>1940–2000</td><td>1940–2000</td><td>1940–2000</td></tr><tr><td>Countries</td><td>47</td><td>47</td><td>47</td><td>47</td></tr><tr><td>F-statistic</td><td></td><td>60.84</td><td></td><td>0.14</td></tr></tbody></table>
* Response of Acemoglu and Johnson (2014)

* Changes in predicted mortality is uncorrelated with past changes in outcomes.

* Cannot distinguish impact of life expectancy in 1940 and subsequent 1940–2000 change using only variation in predicted mortality (mechanical correlation)

* Use 1900 life expectancy, interacted with decade effects: allows initial life expectancy to flexibly affect future growth.
Life expectancy and economic growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Log GDP per Capita</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Log life expectancy (Δ)</td><td>-1.14***<br>[0.27]</td><td>-1.39***<br>[0.36]</td><td>-0.93*<br>[0.49]</td><td>-1.32***<br>[0.63]</td></tr><tr><td>Log LE (1900) × decade</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Log LE (1940) × decade</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Country & year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Estimate</td><td>OLS</td><td>IV</td><td>IV</td><td>IV</td></tr><tr><td>Years</td><td>1940–2000</td><td>1940–2000</td><td>1940–2000</td><td>1940–2000</td></tr><tr><td>Countries</td><td>47</td><td>47</td><td>47</td><td>47</td></tr></tbody></table>
Pollution

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_405.png" /></figure>

<figure><img src="image_406.png" /></figure>

<figure><img src="image_407.png" /></figure>

Toulouse
School of
Economics
* Industrialization and urbanization generated unprecedented pollution with negative consequences on health.

* Pollution, mortality, and the IR in Britain, 1851–1861.
  Beach and Hanlon (2018)

* Pollution, city growth, and the IR in Britain, 1851–1911.
  Hanlon (2020)

* Killer Cities and Industrious Cities? New Data and Evidence on 250 Years of Urban Growth.
  Gindelsky and Jedwab (2023)
# Pollution, mortality, and the Industrial Revolution

<figure><img src="image_408.png" /></figure>

Urban mortality penalty across districts in England and Wales in 1851–1860

Source: Beach and Hanlon (2018).
<figure><img src="image_409.png" /></figure>

Urban mortality penalty across districts in England and Wales in 1851–1860

Source: Beach and Hanlon (2018).
# Pollution, mortality, and the Industrial Revolution

* Urban mortality penalty = common feature of Industrial Revolution in 19th century.

* Urbanization and infectious diseases (unclean water + improper sewage disposal). Kesztenbaum Rosenthal (2011) Alsan and Goldin (2019)

* Poor nutrition. Fogel (2004).

* **Air pollution.** Beach and Hanlon (2018)
# Pollution, mortality, and the Industrial Revolution

<figure><img src="image_410.png" /></figure>

<figure><img src="image_411.png" /><figcaption>Sheffield in 1920</figcaption></figure>

Source: Hanlon (2020) from Richmond (1921).
# Pollution, mortality, and the Industrial Revolution

## Coal production and energy consumption in England, Wales, and Scotland

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr><tr><th></th><th>1560s</th><th>1700-9</th><th>1750-9</th><th>1800-9</th><th>1850-9</th></tr></thead><tbody><tr><th colspan="6">Coal production ('000 tons)</th></tr><tr><th>England</th><td>177</td><td>2,200</td><td>4,295</td><td>11,195</td><td>51,650</td></tr><tr><th>Wales</th><td>20</td><td>140</td><td>220</td><td>1,850</td><td>13,400</td></tr><tr><th>Scotland</th><td>30</td><td>300</td><td>715</td><td>2,000</td><td>9,000</td></tr><tr><th>Total</th><td>227</td><td>2,640</td><td>5,230</td><td>15,045</td><td>74,050</td></tr><tr><th colspan="6">Energy consumption, England and Wales (petajoules)</th></tr><tr><th>Draught animals</th><td>21.1</td><td>32.8</td><td>33.6</td><td>34.3</td><td>50.1</td></tr><tr><th>Population</th><td>14.9</td><td>27.3</td><td>29.7</td><td>41.8</td><td>67.8</td></tr><tr><th>Firewood</th><td>21.5</td><td>22.5</td><td>22.6</td><td>18.5</td><td>2.2</td></tr><tr><th>Wind</th><td>0.2</td><td>1.4</td><td>2.8</td><td>12.7</td><td>24.4</td></tr><tr><th>Water</th><td>0.6</td><td>1.0</td><td>1.3</td><td>1.1</td><td>1.7</td></tr><tr><th>Coal</th><td>6.9</td><td>84.0</td><td>140.8</td><td>408.7</td><td>1,689.1</td></tr><tr><th>Total</th><td>65.1</td><td>168.9</td><td>230.9</td><td>517.1</td><td>1,835.3</td></tr><tr><th>Total less coal</th><td>58.2</td><td>84.9</td><td>90.1</td><td>108.4</td><td>146.2</td></tr></tbody></table>

Source: Wrigley (2010).
# Pollution, mortality, and the Industrial Revolution

<figure><img src="image_412.png" /></figure>

Coal usage shares for the UK in 1855

Source: Hanlon (2018).
# Pollution, mortality, and the Industrial Revolution

* By mid-19th century, coal = main source of power.

* Pollution from burning coal exacerbated:

* Coal burnt inefficiently ⇒ incomplete combustion ⇒ ↑ pollution.

* Coal burnt on-site at factories, located in urban areas.
* By mid-19th century, coal = main source of power.

* Pollution from burning coal exacerbated:

* Coal burnt inefficiently ⇒ incomplete combustion ⇒ ↑ pollution.

* Coal burnt on-site at factories, located in urban areas.

* Serious health implications of coal pollution (respiratory and cardiovascular diseases).

* Yet, little regulation (*laissez faire* ideology).
# Pollution, mortality, and the Industrial Revolution

* Mortality consequences of pollution at district level 1851–1861?
  Beach and Hanlon (2018)
# Pollution, mortality, and the Industrial Revolution

* Mortality consequences of pollution at district level 1851–1861? Beach and Hanlon (2018)

* Mortality data:

* From report of the Registrar General (births, marriages, deaths).

* Decade average 1851–1861 age and cause of death by district (581).

* Primary outcome variable: infant mortality (deaths per 1,000 births).

* Other outcomes: deaths across age categories net of accidents.

* Control for population sorting of less healthy population:

* NPR (not pollution related) mortality: child not pollution related mortality.

* Infant deaths from cholera, digestive disorders, smallpox, typhus.
# Pollution, mortality, and the Industrial Revolution

* Main challenge: no reliable pollution data.
* Main challenge: no reliable pollution data.

* Indirectly capture through local industrial coal use:

* District-level employment by industry (26) in 1851.

* Coal use per worker from Census of Manufacturers 1907.

* Coal use intensity at district *d* level in year *t*:

$$COAL_{dt} = \rho_t \sum_i \theta_i L_{idt}$$

* $L_{idt}$: local employment industry *i* district *t*.

* $\theta_i$: coal use intensity in industry *i*.

* $\rho_t$: efficiency gains in coal use (absorbed, one period).

* Assume stable relative coal intensity per worker across industries.
# Pollution, mortality, and the Industrial Revolution

<figure><img src="image_413.png" /></figure>

Coal use and mortality in England and Wales in 1851–1860

Source: Beach and Hanlon (2018).
<figure><img src="image_414.png" /><figcaption>Coal use and mortality in England and Wales in 1851–1860</figcaption></figure>

Source: Beach and Hanlon (2018).
Pollution, mortality, and the Industrial Revolution

<figure><img src="image_415.png" /></figure>

Coal use and population density in England and Wales in 1851–1860

Source: Beach and Hanlon (2018).
# Pollution, mortality, and the Industrial Revolution

Coal Use per Worker

<figure><img src="image_416.png" /></figure>

Population Density

<figure><img src="image_417.png" /></figure>

Infant Mortality

<figure><img src="image_418.png" /></figure>

## Industrial coal use, population density, and mortality

Source: Beach and Hanlon (2018).
* Baseline specification

$$IMR_d = \alpha_0 + \alpha_1 \log (\text{DENSITY}_d) + \alpha_2 \log (\text{COAL}_d) + X_d \nabla + \varepsilon_d$$

* $IMR_d$: deaths children < 1 in district *d* / births (in 1,000s) 1851–60.

* $\text{DENSITY}_d$: population density in *d* in 1851.

* $\text{COAL}_d$: local industrial coal use in *d*.

* $X_d$: control variables (altitude, hilliness, FLFP, agricultural suitability, seaport indicator and tonnage).
# Pollution, mortality, and the Industrial Revolution

<table><thead><tr><th></th><th colspan="5">DV: Infant mortality rate</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Ln(Coal use)</td><td>15.09***<br>(1.358)</td><td></td><td></td><td>13.68***<br>(2.462)</td><td>10.92***<br>(2.328)</td></tr><tr><td>Ln(Population density)</td><td></td><td>16.55***<br>(1.440)</td><td>7.797***<br>(1.531)</td><td>5.255***<br>(1.432)</td><td>-1.351<br>(1.521)</td></tr><tr><td>Ln(District population)</td><td></td><td></td><td>8.503***<br>(1.421)</td><td>-2.465<br>(2.612)</td><td>-1.924<br>(2.393)</td></tr><tr><td>Child NPR mortality</td><td></td><td></td><td></td><td></td><td>1.500***<br>(0.170)</td></tr><tr><td>Other controls</td><td></td><td></td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>581</td><td>581</td><td>581</td><td>581</td><td>581</td></tr><tr><td>R<sup>2</sup></td><td>0.324</td><td>0.390</td><td>0.498</td><td>0.530</td><td>0.621</td></tr></tbody></table>

Coal use, infant mortality, and the urban mortality penalty

Source: Beach and Hanlon (2018).
# Pollution, mortality, and the Industrial Revolution

* Interpretation (col. 4):

* As population included, coal use interpreted as changes in intensity of district industrial coal use per person.

* Strong correlation between local coal use and infant mortality.

* Coal use explains 1/3 of urban mortality penalty.
# Pollution, mortality, and the Industrial Revolution

* Interpretation (col. 4):

* As population included, coal use interpreted as changes in intensity of district industrial coal use per person.

* Strong correlation between local coal use and infant mortality.

* Coal use explains 1/3 of urban mortality penalty.

* Two concerns with col. 4:

* OVB correlated with local coal use and infant mortality ⇒ robust to additional controls.

* Sorting of less healthy population into more polluted areas ⇒ control for NPR mortality in col. 5.
* Other patterns:

* In cols. 2-3: population density correlated with infant mortality = urban mortality penalty.

* Penalty remains after controlling for coal use (col. 4).

* No penalty when controlling for NPR mortality (col. 5).

⇒ Coal use and NPR both explain infant urban mortality penalty.
* Improve identification by exploiting wind patterns:

* In Britain, wind from south and west toward north and east.

* Upwind and downwind coal use for each district within 25 km.

* Upwind vs downwind unlikely to affect health besides pollution.

* Key statistics: difference between upwind and downwind coal effects (proximity to high coal affects other outcomes).

* Placebo tests: no differential relationship between coal use in upwind and downwind districts and other district characteristics
# Pollution, mortality, and the Industrial Revolution

<table><thead><tr><th></th><th colspan="5">DV: Infant mortality rate</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Ln(Coal use)</td><td>8.727***<br>(1.752)</td><td>8.441***<br>(1.867)</td><td>10.06***<br>(2.855)</td><td>13.63***<br>(2.722)</td><td>10.74***<br>(2.674)</td></tr><tr><td>Ln(Upwind coal)</td><td>1.694<br>(1.228)</td><td>2.849*<br>(1.549)</td><td>2.638*<br>(1.426)</td><td>1.424<br>(1.149)</td><td>1.907*<br>(1.137)</td></tr><tr><td>Ln(Downwind coal)</td><td>-1.285<br>(1.070)</td><td>0.00599<br>(1.489)</td><td>-0.227<br>(1.362)</td><td>-1.420<br>(1.211)</td><td>-1.012<br>(1.173)</td></tr><tr><td>Ln(Population density)</td><td>9.983***<br>(1.486)</td><td>10.68***<br>(1.492)</td><td>10.85***<br>(1.608)</td><td>4.950***<br>(1.615)</td><td>-1.509<br>(1.546)</td></tr><tr><td>Ln(Nearby population)</td><td></td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ln(District population)</td><td></td><td></td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Other controls</td><td></td><td></td><td></td><td>Yes</td><td>Yes</td></tr><tr><td>Child NPR mortality</td><td></td><td></td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>422</td><td>422</td><td>422</td><td>422</td><td>422</td></tr><tr><td>R<sup>2</sup></td><td>0.463</td><td>0.467</td><td>0.467</td><td>0.566</td><td>0.664</td></tr><tr><td>Difference between downwind and upwind coefficients</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coefficient difference</td><td>2.979</td><td>2.843</td><td>2.865</td><td>2.844</td><td>2.919</td></tr><tr><td>p-value</td><td>0.0602</td><td>0.0640</td><td>0.0614</td><td>0.0538</td><td>0.0426</td></tr></tbody></table>

Effect of coal use in upwind and downwind districts

Source: Beach and Hanlon (2018).
Pollution, mortality, and the Industrial Revolution

<table><thead><tr><th></th><th colspan="3">DV: Infant mortality rate</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Ln(Coal use)</td><td>11.80***<br>(1.364)</td><td>12.45***<br>(2.480)</td><td>9.462***<br>(2.275)</td></tr><tr><td>Ln(Coal use) × hilliness</td><td>3.535***<br>(1.091)</td><td>3.783***<br>(0.961)</td><td>4.239***<br>(0.845)</td></tr><tr><td>Ln(Population density)</td><td>6.739***<br>(1.576)</td><td>5.618***<br>(1.434)</td><td>-1.129<br>(1.454)</td></tr><tr><td>Ln(District population)</td><td></td><td>-1.842<br>(2.543)</td><td>-1.210<br>(2.262)</td></tr><tr><td>Mean altitude</td><td>1.149<br>(1.730)</td><td>-0.737<br>(1.744)</td><td>0.768<br>(1.672)</td></tr><tr><td>Hilliness</td><td>-9.334***<br>(1.761)</td><td>-9.496***<br>(1.888)</td><td>-7.834***<br>(1.961)</td></tr><tr><td>Other controls</td><td></td><td>Yes</td><td>Yes</td></tr><tr><td>Child NPR mortality control</td><td></td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>581</td><td>581</td><td>581</td></tr><tr><td>R<sup>2</sup></td><td>0.527</td><td>0.549</td><td>0.645</td></tr></tbody></table>

Effect of coal use interacted with hilly topography

Source: Beach and Hanlon (2018).
# Pollution, mortality, and the Industrial Revolution

* Use wind patterns to recover within-district effects of pollution.

* Need estimate of how concentration of coal-based pollution diminishes with distance.

* Use pollution dispersion modelling for 50m×50m cells across 20km×20km grid with smokestack at center.

* Average concentration in district = 4 times downwind and 1.5 times in upwind.

* ↑ log-point industrial coal use ⇒ ↑ 7-10 deaths per 1,000 live births.

* Little upward bias in baseline results.
# Pollution, mortality, and the Industrial Revolution

<table><thead><tr><th colspan="11">DV: Mortality rate in each age category (per 1,000 persons)</th></tr><tr><th>Under 5</th><th>5-9</th><th>10-14</th><th>15-19</th><th>20-24</th><th>25-34</th><th>35-44</th><th>45-54</th><th>55-64</th><th>65 up</th></tr></thead><tbody><tr><td colspan="10">Coefficient on coal use in upwind districts</td></tr><tr><td>0.681<br>(0.416)</td><td>0.102*<br>(0.0547)</td><td>-0.0002<br>(0.0433)</td><td>0.0804<br>(0.0643)</td><td>0.168<br>(0.103)</td><td>0.0806<br>(0.0828)</td><td>-0.224**<br>(0.112)</td><td>-0.173<br>(0.128)</td><td>-0.136<br>(0.170)</td><td>-0.161<br>(0.360)</td></tr><tr><td colspan="10">Coefficient on coal use in downwind districts</td></tr><tr><td>-0.756<br>(0.477)</td><td>-0.0188<br>(0.0595)</td><td>-0.0504<br>(0.0411)</td><td>-0.0782<br>(0.0741)</td><td>-0.0943<br>(0.0864)</td><td>-0.139*<br>(0.0761)</td><td>-0.248*<br>(0.128)</td><td>-0.200<br>(0.141)</td><td>-0.348**<br>(0.173)</td><td>-0.403<br>(0.314)</td></tr><tr><td colspan="10">Difference between upwind and downwind coefficients</td></tr><tr><td>1.437</td><td>0.1208</td><td>0.0502</td><td>0.1586</td><td>0.2623</td><td>0.2196</td><td>0.024</td><td>0.027</td><td>0.212</td><td>0.242</td></tr><tr><td colspan="10">F-test for significance of difference between upwind and downwind effects (F-stat & p-value)</td></tr><tr><td>6.14<br>0.0136</td><td>2.71<br>0.1004</td><td>0.88<br>0.3495</td><td>4.8<br>0.029</td><td>5.62<br>0.0183</td><td>8.41<br>0.0039</td><td>0.05<br>0.8176</td><td>0.05<br>0.8284</td><td>1.28<br>0.2588</td><td>0.53<br>0.4652</td></tr></tbody></table>

Source: Beach and Hanlon (2018).

## Upwind vs downwind coal use effects by age group
# Pollution, city growth, and the Industrial Revolution

* How does industrial structure affect city growth?

* +: job growth attracts population.

* -: pollution deters population due to ↓ quality of amenities + ↓ productivity.
# Pollution, city growth, and the Industrial Revolution

* How does industrial structure affect city growth?

* +: job growth attracts population.

* -: pollution deters population due to ↓ quality of amenities + ↓ productivity.

* Assess impact of pollution on growth of British cities 1851–1911.
Hanlon (2020)
# Pollution, city growth, and the Industrial Revolution

* How does industrial structure affect city growth?

* +: job growth attracts population.

* -: pollution deters population due to ↓ quality of amenities + ↓ productivity.

* Assess impact of pollution on growth of British cities 1851–1911.
Hanlon (2020)

* Challenge 1. Pollution is one of many factors + effects take years: need high variation over multiple decades.

* Challenge 2. No reliable pollution data: need proxy.

* Challenge 3. Separate positive from negative effects: need empirical design.
# Pollution, city growth, and the Industrial Revolution

* On pollution context: remember Sheffield 1920.

* On migration context: British population highly mobile.

* Large flows of population from rural areas, Ireland, Scotland, to English cities.

* 1851–1881: 1/4 people changed county of residence; 1/2 changed town. Long and Ferrie (2003)

* Primary driver of migration flows: search for work.
# Pollution, city growth, and the Industrial Revolution

<figure><img src="image_419.png" /><figcaption>British coal consumption, 1854–1910</figcaption></figure>

Source: Hanlon (2020) based on Mitchell (1988).
<figure><img src="image_420.png" /><figcaption>British coal consumption by use, 1830–1913</figcaption></figure>

Source: Hanlon (2020) based on Mitchell (1988).
# Pollution, city growth, and the Industrial Revolution

* Local industrial composition:

* Occupations 1851–1911 for 31 largest cities from Census.

* Series 1851–1911 for 26 broad industries.
* Local industrial composition:

* Occupations 1851–1911 for 31 largest cities from Census.

* Series 1851–1911 for 26 broad industries.

* Coal intensity for each industry:

* Coal use per worker and industry from Census of Production 1907.

* Coal use intensity in city c in year t:

$$COAL_{ct} = \rho_t \sum_i \theta_i L_{ict}$$

* $L_{ict}$: local employment industry i in city t.

* $\theta_i$: coal use intensity in industry i.

* $\rho_t$: efficiency gains in coal use.
English cities included in the study

<figure><img src="image_421.png" /></figure>

Source: Hanlon (2020).
## Industry coal use per worker (tons per worker)

<table><thead><tr><td>Industry</td><td>Coal/ worker</td></tr></thead><tbody><tr><td>Earthenware, bricks, etc.</td><td>48.9</td></tr><tr><td>Metal and engine manufacturing*</td><td>43.7</td></tr><tr><td>Chemical and drug manufacturing</td><td>40.1</td></tr><tr><td>Mining</td><td>28.9</td></tr><tr><td>Oil, soap, etc. production</td><td>20.7</td></tr><tr><td>Brewing and beverage production</td><td>19.4</td></tr><tr><td>Leather, hair goods production</td><td>12.1</td></tr><tr><td>Food processing</td><td>12.0</td></tr><tr><td>Textile production</td><td>10.1</td></tr><tr><td>Paper and publishing</td><td>9.7</td></tr><tr><td>Shipbuilding</td><td>6.1</td></tr><tr><td>Wood furniture, etc., production</td><td>5.4</td></tr><tr><td>Vehicle production</td><td>2.6</td></tr><tr><td>Instruments, jewelry, etc.</td><td>2.0</td></tr><tr><td>Apparel</td><td>1.6</td></tr><tr><td>Tobacco products</td><td>1.1</td></tr></tbody></table>

Source: Hanlon (2020).
* Estimating coal use effects on city-industry employment growth:

$$ \Delta \log(L_{ict}) = b_0 + b_1 \Delta \log(C_{ct}) + b_2 \Delta \log(L_{ct}) + \zeta_{it} + e_{ict} $$

* $L_{ct}$: overall city employment growth (negative congestion or positive agglomeration effects).

* $C_{ct}$: city coal use (negative pollution or productivity effects).

* $\zeta_{it}$: industry-time fixed effects (absorb industry-specific productivity and demand shocks).

* Use log because linear relationship between pollution and employment in log.
# Pollution, city growth, and the Industrial Revolution

* **Identification issue:**

* Changes in overall city employment and change in city coal use endogenously affected by city-industry employment growth.

* Replace these terms with predicted values based on national-level trends and use deviations from predicted trends.
* Overall city employment:

$$ \Delta \log (\text{PrCityEMP}_{ct}) = \log \left( \sum_{j \neq i} L_{jct-\tau} \times \text{GR}_{j-ct,t-\tau} \right) - \log \left( \sum_{j \neq i} L_{jct-\tau} \right) $$

* $\text{GR}_{j-ct,t-\tau}$: growth rate of industry i in all cities other than c, from t-τ to t.

* $\Delta \log (\text{PrCityEMP}_{ct})$: expected growth in employment in all other local industries, given national industry growth rates and initial industrial composition of the city.

* When studying industry i, it is dropped from the measure.
* City coal use:

$$ \Delta \log (\text{PredCoal}_{ct}) = \log \left( \sum_{j \neq i} L_{jct-\tau} \times \text{GR}_{j-ct, t-\tau} \times \theta_j \right) - \log \left( \sum_{j \neq i} L_{jct-\tau} \times \theta_j \right) $$

* Difference due only to variation in coal intensity of industries $\theta_j$.
$$ \text{DEVIATION} = \Delta \log (\text{CityEMP}_{ct}) - \Delta \log (\text{PrCityEMP}_{ct}) $$

* DEVIATION: difference between actual change in city employment and expected change given city's initial industrial structure and industry growth rates in all other cities.

* Captures extent to which employment growth of city performs relative to predictions based on national industry growth rates.
# Pollution, city growth, and the Industrial Revolution

<figure><img src="image_422.png" /></figure>

Deviation vs predicted change in city coal use (over 2 decades)

* In cities where expect rising coal use, city employment growth systematically underperforms.
* Estimating coal use effects city-industry employment growth:

$$ \Delta \log(L_{ict}) = b_0 + b_1 \Delta \log(\text{PrCoal}_{ct}) + b_2 \Delta \log(\text{PrCityEmp}_{ct}) + \zeta_i + e_{ict} $$

* Predicted employment picks up direct effects of employment growth in other industries in city c on employment growth of i due to congestion or agglomeration.

* $b_1$ captures additional impact generated when this employment growth occurs in more coal-intensive industries.

* $b_1$ is the impact of a rise in local industrial coal use holding constant overall local employment level.

* $b_2$ is the impact of increase in local employment holding fixed level of local industrial coal use.
<table><caption>DV: Δ ln of city industry employment</caption><thead><tr><th rowspan="2">Difference:</th><th colspan="3">All industries</th><th colspan="3">Manufacturing industries</th></tr><tr><th>One decade (1)</th><th>Two decades (2)</th><th>Three decades (3)</th><th>One decade (4)</th><th>Two decades (5)</th><th>Three decades (6)</th></tr></thead><tbody><tr><td>Δln(PredCoal)</td><td>-0.611<br>(0.621)</td><td>-1.987***<br>(0.732)</td><td>-3.016***<br>(0.803)</td><td>-0.444<br>(0.685)</td><td>-2.218***<br>(0.632)</td><td>-3.257***<br>(0.813)</td></tr><tr><td>Δln(PrCityEMP)</td><td>-0.536<br>(0.586)</td><td>0.392<br>(0.757)</td><td>1.362*<br>(0.826)</td><td>-0.725<br>(0.528)</td><td>0.383<br>(0.553)</td><td>1.172*<br>(0.692)</td></tr><tr><td>Ind.-time effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>4,809</td><td>4,012</td><td>3,208</td><td>2,773</td><td>2,312</td><td>1,849</td></tr><tr><td>R<sup>2</sup></td><td>0.259</td><td>0.355</td><td>0.429</td><td>0.246</td><td>0.336</td><td>0.403</td></tr></tbody></table>

Baseline city-industry regression results

Source: Hanlon (2020).
# Pollution, city growth, and the Industrial Revolution

* Coal use has a negative impact on city-industry employment growth over the long run (2 or 3 decades).

* Negative short-run effect of employment growth in other city-industries that becomes positive over time: negative congestion forces weaken while positive city-size agglomeration effects strengthen over time.
# Pollution, city growth, and the Industrial Revolution

* Coal use has a negative impact on city-industry employment growth over the long run (2 or 3 decades).

* Negative short-run effect of employment growth in other city-industries that becomes positive over time: negative congestion forces weaken while positive city-size agglomeration effects strengthen over time.

* Aggregate effects on city working population are negative.
<table><caption>DV: Δ ln of city employment in analysis industries (two decade differences)</caption><thead><tr><th rowspan="2"></th><th colspan="2">City employment<br>in analysis industries</th><th colspan="2">Total city<br>working population</th><th colspan="2">Total city<br>population</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Δln (PrWorkpop<sub>ct</sub>)</td><td>0.955<br>(0.666)</td><td>0.433<br>(0.726)</td><td>0.756<br>(0.664)</td><td>0.0795<br>(0.724)</td><td>0.385<br>(0.624)</td><td>-0.229<br>(0.730)</td></tr><tr><td>Δln (PrCoal<sub>ct</sub>)</td><td>-1.457**<br>(0.657)</td><td>-1.655**<br>(0.670)</td><td>-1.352**<br>(0.650)</td><td>-1.400**<br>(0.665)</td><td>-0.986<br>(0.633)</td><td>-1.055<br>(0.686)</td></tr><tr><td>Time effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Other controls</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td></tr><tr><td>Observations</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td></tr><tr><td>R<sup>2</sup></td><td>0.067</td><td>0.202</td><td>0.084</td><td>0.208</td><td>0.099</td><td>0.213</td></tr></tbody></table>

City-level regression results

Source: Hanlon (2020).
* Consumer disamenities or productivity effects?

* Need location-specific wage, rent, and price data.

* Available for 51 cities in 1905 report of Board of Trade.

* Results:

* Coal has small negative impact on quality of life.

* Impact of coal seems larger through productivity effects.
Gindelsky and Jedwab (2022)

* Urbanization during the IR has been linked to high mortality rates in the UK (urban mortality penalty).

* Rising labor productivity and wages tied to the industrious revolution: higher working hours and lower birth rates.

* But despite this double demographic penalty, cities industrialized and grew.
## Gindelsky and Jedwab (2022)

* Urbanization during the IR has been linked to high mortality rates in the UK (urban mortality penalty).

* Rising labor productivity and wages tied to the industrious revolution: higher working hours and lower birth rates.

* But despite this double demographic penalty, cities industrialized and grew.

* Assess the extent to which the demographic urban penalty changed between 1700 and 1950 worldwide.
* Data:

* Sample of 142 cities with 200k+ inhabitants in 1900.

* Demography indicators for each city-decade pair from 300 sources:

* CDR. Crude death rate (death per 1000 inhabitants).

* CBR. Crude birth rate (births per 1000 inhabitants).

* CRNI. Crude rate of natural increase: CBR-CDR.

* Population.

* Timing of the IR (Britain = 1760, USA = 1790, France=1810).
Killer cities and industrious cities

<figure><img src="image_423.png" /></figure>

Largest cities of the world circa 1900

Source: Gindelsky and Jedwab (2022)).
Killer cities and industrious cities

<figure><img src="image_424.png" /><figcaption>(b) Share of Pop. with an Available Obs.</figcaption></figure>

<figure><img src="image_425.png" /></figure>

Share of sample with an available observation 1700–1950

Source: Gindelsky and Jedwab (2022)).
<figure><img src="image_426.png" /></figure>

Diffusion of the IR among the 142 cities

Source: Gindelsky and Jedwab (2022)).
Killer cities and industrious cities

<figure><img src="image_427.png" /></figure>

Evolution of city crude death rates

Source: Gindelsky and Jedwab (2022)).
Killer cities and industrious cities

<figure><img src="image_428.png" /></figure>

Evolution of city crude birth rates

Source: Gindelsky and Jedwab (2022)).
<figure><img src="image_429.png" /></figure>

Evolution of city demographic rates

Source: Gindelsky and Jedwab (2022)).
<figure><img src="image_430.png" /><figcaption>Evolution of city population size</figcaption></figure>

Source: Gindelsky and Jedwab (2022)).
# Killer cities and industrious cities

* Rates of natural increase increased as city size increased during the IR era (1810–1910).

* The urban penalty may have decreased during the industrial era.
# Killer cities and industrious cities

* Rates of natural increase increased as city size increased during the IR era (1810–1910).

* The urban penalty may have decreased during the industrial era.

* Negative health effects leading to higher mortality:

* Air and water pollution.

* Congestion effects and rapid spread of diseases.
* Rates of natural increase increased as city size increased during the IR era (1810–1910).

* The urban penalty may have decreased during the industrial era.

* Negative health effects leading to higher mortality:

* Air and water pollution.

* Congestion effects and rapid spread of diseases.

* Positive effects leading to lower mortality (outweighting negative effects):

* Standards of living increased (see GDP estimates, welfare ratios...).

* Improved nutrition, public health investments (sewer systems), health care, education.
Pandemics in Historical Perspective

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_431.png" /></figure>

<figure><img src="image_432.png" /></figure>

<figure><img src="image_433.png" /></figure>

Toulouse
School of
Economics
What can history teach us about the potential long-run consequences of COVID-19?
* Most serious health and economic crisis of 21st century:

* Since December 2019: nearly 7M fatalities, 705M cases.

* No natural immunity, vaccine administered since late December 2020.

* Large-scale non-pharmaceutical interventions.

* Large economic downturn, e.g., 2020 Q2/2019 Q2: France –19%; USA –30%.

* 2020 GDP growth: Euro area –7%; USA –2.2%

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

* Since December 2019: nearly 7M fatalities, 705M cases.

* No natural immunity, vaccine administered since late December 2020.

* Large-scale non-pharmaceutical interventions.

* Large economic downturn, e.g., 2020 Q2/2019 Q2: France –19%; USA –30%.

* 2020 GDP growth: Euro area –7%; USA –2.2%

⇒ Critical juncture.

* Economic history can provide some guidance.
Cumulative confirmed COVID-19 cases and deaths, World

Limited testing and challenges in the attribution of cause of death mean the confirmed case and death counts may not reflect the true counts.

Our World
in Data

<figure><img src="image_434.png" /></figure>
* 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_435.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.
* 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_436.png" /><figcaption>Worldwide Spread of 1918–1919 Influenza Pandemic</figcaption></figure>

Data for 48 countries from Barro, Ursua, and Weng (2020).
# Epidemiology of COVID-19: Rate of Transmission

* 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 worldwide; 1.9–2.6 in W. Europe.
  Petersen et al. (2020) Locatelli et al. (2021)

* 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

<figure><img src="image_437.png" /></figure>

## 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 $\approx$ 2% (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 ≈ 2% (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 ≈ 2% (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

## Case fatality rate of the ongoing COVID-19 pandemic

The Case Fatality Rate (CFR) is the ratio between confirmed deaths and confirmed cases.

During an outbreak of a pandemic the CFR is a poor measure of the mortality risk of the disease. We explain this in detail at OurWorldData.org/Coronavirus

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Source: Johns Hopkins University CSSE COVID-19 Data - Last updated 26 October, 09:04 (London time)

Powered by ourworldindata.org

Note: Only countries with more than 100 confirmed cases are included. Only countries with more than 100 confirmed cases are included.

Data from ECDC and ourworldindata.org.
<figure><img src="image_440.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

<figure><img src="image_441.png" /></figure>

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

Figure from Murray et al. (2006)
* Absent of cure (until 2021) and limited medical capacities:
  ⇒ Widespread non-pharmaceutical interventions (NPIs).
* Absent of cure (until 2021) 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 (until 2021) 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.
# The 1918 Influenza Pandemic

* 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 (2022)
# 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_442.png" /><figcaption>(a) Mortality, 1915-1925</figcaption></figure>

<figure><img src="image_443.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_444.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 $\simeq$ 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_445.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.
# What Were the Economic Effects of 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.
* 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_446.png" /></figure>

NPI Intensity and log manufacturing employment

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

<figure><img src="image_447.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?
# 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.
## 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.
Were There Scarring Effects of Influenza?

<figure><img src="image_448.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_449.png" /></figure>

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

<figure><img src="image_450.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_451.png" /><figcaption>1970 Poverty Rates by Year of Birth</figcaption></figure>

Figure from Beach, Clay, and Saavedra (2020) using results from Almond (2006)
* 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)
* 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 (2022)
* 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 (2022)

* 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 (2022)

## 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 (2022)

* 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_452.png" /></figure>

The Great Depression in the U.S.

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

* 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_453.png" /></figure>

## Discontinuity in labor market conditions

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

<figure><img src="image_454.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.
# Scarring Effects of the Great Depression?

## Duque and Schmitz (2023)

* Instead of labor market entry scarring, look at in utero up to age 16.

* Compare individuals exposed to varying levels of economic shocks in their childhood years.

* Measurement of shock: state-year wage index while in utero.

* Use longitudinal data from Health and Retirement Study (1992–2018): 7k individuals born 1929–40 with information on health, education, earnings, labor, and childhood environment.
# Scarring Effects of the Great Depression?

<figure><img src="image_455.png" /><figcaption>Variation in the Wage Index across states (1929–40)</figcaption></figure>
# Scarring Effects of the Great Depression?

* Difference-in-differences specification with leads and lags.

$$Y_{isrct} = \alpha + \sum_{t=-3}^{T=16} \beta_t Wages_{sc} + X'_i \delta + Z_{sc} + \theta_s + \eta_c + \lambda_t + \gamma_{rc} + \varepsilon_{isc}$$

* $Y_{isrct}$: 50+ year old outcome of individual i born in state s, region r, year c, observed in year t in HRS.

* $Wages_{sc}$: wage index at state and year level for age t of individual i.

* $X_i$: individual characteristics (gender, age, maternal education, rural, father present).

* $Z_{sc}$: pre-1930 state characteristics (infant mortality, maternal mortality, farmland) interacted with birth year FE.

* $\theta_s$: state FE. $\eta_c$: birth year FE. $\lambda_t$: HRS year FE.

* Assess effect of economic conditions throughout childhood with placebo leads (pre-birth).
# Scarring Effects of the Great Depression?

<figure><img src="image_456.png" /></figure>

## Effect of wage index on economic index

* Later-life economic outcomes negatively affected by economic conditions when 0–2.

* Magnitude: ↓ 1 SD economic conditions when 0–2 ⇒ ↓ 0.1 SD economic outcomes when 50+.

Figure from Duque and Schmitz (2023).
# Scarring Effects of the Great Depression?

<figure><img src="image_457.png" /></figure>

Effect of wage index on number of chronic disease conditions

* Later-life health outcomes negatively affected by economic conditions when in utero.

* Magnitude: ↓ 1 SD economic conditions when in utero ⇒ ↑ 0.15 SD health outcomes when 50+.

Figure from Duque and Schmitz (2023).
# Scarring Effects of the Great Depression?

## Other results:

* Economic effects driven by lower probability of professional job and age of retirement.

* Earnings effects especially after age 50.

* Health effects stronger the older people get (70s vs 60s).

* Survival rates also lower.
# Scarring Effects of the Great Depression?

## Other results:

* Economic effects driven by lower probability of professional job and age of retirement.

* Earnings effects especially after age 50.

* Health effects stronger the older people get (70s vs 60s).

* Survival rates also lower.

## Mechanisms (using census data 1940–90 for 1929–40 cohorts):

* Lower family income and home ownership rates.

* Lower education (high school completion).

* No effect through migration.
* 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.
* 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?
# 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.

* 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?