# TSE M2 ETE Development: Theory, Public Policy, and Historical Perspectives (2022-2023) Topics in Economic History

Victor Gay

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Victor Gay. TSE M2 ETE Development: Theory, Public Policy, and Historical Perspectives (2022-2023) Topics in Economic History. Doctoral. Development: Theory, Public Policy, and Historical Perspectives, France. 2023, pp.606. hal-04063637

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Development: Theory, Public Policy, and
Historical Perspectives
Topics in Economic History

Victor Gay

University of Toulouse 1 Capitole

Toulouse School of Economics

Institute for Advanced Study in Toulouse

M2 PPD, Semester 2, 2023

<figure><img src="image_2.png" /></figure>
Topic 1
Economics and the modern economic historian
* Does the past have useful economics?

* A classical approach. McCloskey (1976)

* From classical to modern approach. Arrow (1985) Solow (1985)

* A modern approach. Abramitzky (2015) Cantoni Yuchtman (2021)

* Trends in the field.

* Trends in publishing. Abramitzky (2015) Cioni et al. (2019)

* Historical econometrics. Valencia Caicedo (2021)

* Future trends. Lamoreaux (2015)
## McCloskey (1976)

* Economic history for itself.

* More and better economic facts.

* Better economic theory.

* Better economic policy.
"Disinterested intellectual curiosity is the lifeblood of real civi-
lization. [...] There is nothing that more divides civilized from
semi-savage man than to be conscious of our forefathers as they
really were, and bit by bit to reconstruct the mosaic of the long-
forgotten past. To weigh the stars, or to make ships sail in the air
or below the sea, is not a more astonishing and ennobling perfor-
mance on the part of the human race in these latter days, than
to know the course of events that had long been forgotten, and
the true nature of men and women who were here before us."
Trevelyan (1942)
"Disinterested intellectual curiosity is the lifeblood of real civi-
lization. [...] There is nothing that more divides civilized from
semi-savage man than to be conscious of our forefathers as they
really were, and bit by bit to reconstruct the mosaic of the long-
forgotten past. To weigh the stars, or to make ships sail in the air
or below the sea, is not a more astonishing and ennobling perfor-
mance on the part of the human race in these latter days, than
to know the course of events that had long been forgotten, and
the true nature of men and women who were here before us."
Trevelyan (1942)

"Every economy and every society has an interesting story to tell."
Mokyr (2003)

⇒ Economic history much alike mathematical economics.
* Inductive or deductive approach: facts are necessary.
* Inductive or deductive approach: facts are necessary.

* History simply provides more facts.

* Whenever institutions are present, statistics are present.

* Even before: archaeology provides facts about the economy.
* More facts about economic growth:

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

* Maddison project database: real GDP since 1000.
* More facts about economic growth:

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

* Maddison project database: real GDP since 1000.

* More facts about populations:

* IPUMS USA: full count censuses 1850–1950.

* IPUMS NAPP: full count censuses North Atlantic starting 1703.
* More facts about economic growth:

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

* Maddison project database: real GDP since 1000.

* More facts about populations:

* IPUMS USA: full count censuses 1850–1950.

* IPUMS NAPP: full count censuses North Atlantic starting 1703.

* Other facts:

* Macrohistory: macro-finance indicators, 17 countries since 1870.

* World Inequality Database: inequality indices since 1875.
Ruggles et al. (2015)

* Greatest accomplishment for empirical economics.

* IPUMS: 98 countries, 443 censuses, > 1Bn individuals.

* Historical samples:

* USA: full count censuses 1850–1950.

* UK: 1851–1911.

* Canada: 1852–1911.

* Norway: 1801–1910.

* Sweden: 1880–1910.

* Iceland: 1703–1910.

* Data management:

* Data preservation and reformatting.

* Metadata integration: harmonized documentation.

* Variable integration, e.g., marital status.

* Dissemination: free, accessible, formats.
* Historical data are routinely richer than modern data.
* Historical data are routinely richer than modern data.

* Full access to companies' records and manufacturing censuses.

* Populations: no anonymity rule.

* USA: linked samples across censuses (IPUMS).

* France: linked samples across vital records (TRA).
* Historical data are routinely richer than modern data.

* Full access to companies' records and manufacturing censuses.

* Populations: no anonymity rule.

* USA: linked samples across censuses (IPUMS).

* France: linked samples across vital records (TRA).

* Access dramatically improved recently.
* Main limits to more and better facts:

* Existence of historical data.

* Inaccuracies and measurement errors.
* Main limits to more and better facts:

* Existence of historical data.

* Inaccuracies and measurement errors.

* On existence.

* True.

* But be creative and knowledgeable about the data production process.
* Main limits to more and better facts:

* Existence of historical data.

* Inaccuracies and measurement errors.

* On existence.

* True.

* But be creative and knowledgeable about the data production process.

* On inaccuracies.

* Less true.

* Much more cautious approach to data as constructed objects.

> "Much of the problem, I think, arises because of the separation in economics between data producers and data analyzers. By and large, we do not produce our own data and, hence, do not feel responsible for it." - Griliches (1974)
* History provides better facts through natural experiments.

* Can we learn from these experiments?

* People were the same hundred years ago.

* Structures were different and changed: we can learn a lot from that.

⇒ More on that later.
* Obvious that economic history uses economic theory in Popperian sense (as all applied economics).

* The converse is less obvious but no less true.
* Obvious that economic history uses economic theory in Popperian sense (as all applied economics).

* The converse is less obvious but no less true.

> In analogous discussions concerning the role of theory in historical research the argument is frequently made (perhaps because it is valid) that the historian will inevitably be guided by some [theory].
> 
> [...] Much the same can be said for the use of history by theorists.
> 
> Even the most scornful ahistorical economist makes some use of history: his own experience, the experience of his generation, or the loose historical generalizations which abound in the folklore of even highly sophisticated societies.

Cameron (1965)
* Theorists routinely use history (although some get facts wrong):

* Kaldor stylized facts and neoclassical growth theory.

* Ricardo and rising land rents.

* Rostow and the stages of growth.

* Marx and the impoverishment of the working class.
* Economic history to test economic theory.
* Economic history to test economic theory.

* More helpful than contemporaneous economy.

* Enables to test theories in their pure form, as structures sometimes less complex in the past.

* E.g., Koudijs (2015) on information and stock prices volatility.

"Have a very healthy respect for the study of economic history, because that's the raw material out of which any of your conjectures or testings will come." Samuelson (2009)
*One purpose of history is to broaden our conception of the possible.*

Rockoff (1975)

* Learn from historical crises to inform policy.

* Bernanke is among the best examples.
* Smith, Marshall, Schumpeter, Keynes, Friedman...

* The great economists were all deeply historical in their thinking.

* Even if not object of study, the long view helps finding important research questions.
* Smith, Marshall, Schumpeter, Keynes, Friedman...

* The great economists were all deeply historical in their thinking.

* Even if not object of study, the long view helps finding important research questions.

* So read, read, read!
Does the past have useful economics?

* A classical approach. McCloskey (1976)

* From classical to modern approach. Arrow (1985) Solow (1985)

* A modern approach. Abramitzky (2015) Cantoni Yuchtman (2021)
## Arrow (1985) Solow (1985)

* The (caricatured) classical approach might be a naive view of history.

* History is not just a repository of facts for testing economic theory.

*The aim of historical study as such was not simply to serve as a source of data from which to infer and to test social science generalizations.*
## Arrow (1985) Solow (1985)

* The (caricatured) classical approach might be a naive view of history.

* History is not just a repository of facts for testing economic theory.

> The aim of historical study as such was not simply to serve as a source of data from which to infer and to test social science generalizations.

* Economic history would just be applied economics with old data.

> In this dispensation an economic historian is merely an economist with a high tolerance for dust or a working knowledge of a foreign language.
* Is economics like physics, true for all time, or are its laws historically conditioned?
* Is economics like physics, true for all time, or are its laws historically conditioned?

* By its general statements, economic theory generates new questions for economic history.
* Is economics like physics, true for all time, or are its laws historically conditioned?

* By its general statements, economic theory generates new questions for economic history.

* But economic theory emphasizes generality at the expense of particularity.

* Economic history helps the development of economic analysis by defining its historical conditioning.

⇒ Economic history is crucial to assess “external validity.”
Does the past have useful economics?

* A classical approach. McCloskey (1976)

* From classical to modern approach. Arrow (1985) Solow (1985)

* A modern approach. Abramitzky (2015) Cantoni Yuchtman (2021)
Abramitzky (2015)

* Test economic theory.

* Improve economic policy.

* Learn about mechanisms.

* Answer the big questions.
* Economic history to test economic theory.
* Economic history to test economic theory.

* Expands possibilities of exogenous variation for identification:
* Economic history to test economic theory.

* Expands possibilities of exogenous variation for identification:

* Role of market access for economic development: Redding and Sturm (2008) use the division and reunification of Germany.
* Economic history to test economic theory.

* Expands possibilities of exogenous variation for identification:

* Role of market access for economic development: Redding and Sturm (2008) use the division and reunification of Germany.

* Role of news for stock price fluctuations: Koudijs (2015) uses delay of sailing boats due to adverse weather in 18th century.
* Economic history to test economic theory.

* Expands possibilities of exogenous variation for identification:

* Role of market access for economic development: Redding and Sturm (2008) use the division and reunification of Germany.

* Role of news for stock price fluctuations: Koudijs (2015) uses delay of sailing boats due to adverse weather in 18th century.

* Impact of supply shocks on technological change: Hanlon (2015) uses drop in cotton from US South after Civil War.
* Economic history to test economic theory.

* Expands possibilities of exogenous variation for identification:

* Role of market access for economic development: Redding and Sturm (2008) use the division and reunification of Germany.

* Role of news for stock price fluctuations: Koudijs (2015) uses delay of sailing boats due to adverse weather in 18th century.

* Impact of supply shocks on technological change: Hanlon (2015) uses drop in cotton from US South after Civil War.

* Effect of ↓ agricultural labor availability on agricultural development: Hornbeck and Naidu (2014) use Great Mississippi Flood of 1927.
* Economic history to improve economic policy.
* Economic history to improve economic policy.

* Historical settings allow counterfactual worlds, or repetition of episodes.

* Again, past financial crises deeply inform current policy.
* Economic history to learn about mechanisms.

* Typically, economists learn about mechanisms indirectly, e.g. by interactions or heterogeneity across subgroups.

* Deep knowledge of historical setting and direct (qualitative) historical evidence can be much more convincing.

* Analytic narratives approach using game theory (see Avner Greif's work).
* Economic history to answer the big questions.
* Economic history to answer the big questions.

* Only by looking at history can they be answered:
* Economic history to answer the big questions.

* Only by looking at history can they be answered:

* Why are some countries rich and other poor?

* Why did the Industrial Revolution happen in Europe and not elsewhere?

* What factors caused the demographic transition?

* How and why did inequality evolve over time?

* What is the process of cultural change?
* Economic history to answer the big questions.

* Only by looking at history can they be answered:

* Why are some countries rich and other poor?

* Why did the Industrial Revolution happen in Europe and not elsewhere?

* What factors caused the demographic transition?

* How and why did inequality evolve over time?

* What is the process of cultural change?

* These are too big to nail. But...

> Economic history seems to collectively agree to not limit ourselves to answer only those questions with random variation, of which there are not as many.
Cantoni Yuchtman (2021)

* Economic history is especially popular in mainstream economics for natural experiments.

* Enable to answer important questions that require historical perspective while keeping applied micro approach and credible causal identification.

* Especially popular in economic growth and development.

⇒ Bridge between economic history and all other disciplines.
* Taxonomy of three primary motives for examining historical natural experiments:

* Natural experiments to learn about historical episodes (cat. 1).

* Natural experiments yielding general economic insights (cat. 2).

* Natural experiments to understand contemporary development (cat. 3).
* Taxonomy of three primary motives for examining historical natural experiments:

* Natural experiments to learn about historical episodes (cat. 1).

* Natural experiments yielding general economic insights (cat. 2).

* Natural experiments to understand contemporary development (cat. 3).

* Not mutually exclusive: most research overlaps categories.

* No one motive is better or worse.
* This focus is the result of changes across several fields.
* This focus is the result of changes across several fields.

* Labor economics and the credibility revolution.
* This focus is the result of changes across several fields.

* Labor economics and the credibility revolution.

* Push toward exogenous sources of variation for identification of causal effects. Angrist Pischke (2010)

* Historical variation in public policies, historical human-made or natural events provide better sources of variation for identification (category 2).
* This focus is the result of changes across several fields.

* Labor economics and the credibility revolution.

* Push toward exogenous sources of variation for identification of causal effects. Angrist Pischke (2010)

* Historical variation in public policies, historical human-made or natural events provide better sources of variation for identification (category 2).

* Economic history beyond the cliometric revolution (category 1).
  ⇒ More on the cliometric revolution later
* This focus is the result of changes across several fields.

* Labor economics and the credibility revolution.

* Push toward exogenous sources of variation for identification of causal effects. Angrist Pischke (2010)

* Historical variation in public policies, historical human-made or natural events provide better sources of variation for identification (category 2).

* Economic history beyond the cliometric revolution (category 1).
  ⇒ More on the cliometric revolution later

* Development and the randomization revolution (category 3).
* This focus is the result of changes across several fields.

* Labor economics and the credibility revolution.

* Push toward exogenous sources of variation for identification of causal effects. Angrist Pischke (2010)

* Historical variation in public policies, historical human-made or natural events provide better sources of variation for identification (category 2).

* Economic history beyond the cliometric revolution (category 1).
  ⇒ More on the cliometric revolution later

* Development and the randomization revolution (category 3).

⇒ At the confluence: AJR's *Colonial Origins*
* Seek historical experiments within pre-specified setting.

* Aim at more compelling causal arguments about process of interest, not testing general economic hypothesis.
* Seek historical experiments within pre-specified setting.

* Aim at more compelling causal arguments about process of interest, not testing general economic hypothesis.

* Examples:

* Impact of the invention of the printing press on city growth: quasi-random diffusion of the press from Mainz (Dittmar 2011).
* Seek historical experiments within pre-specified setting.

* Aim at more compelling causal arguments about process of interest, not testing general economic hypothesis.

* Examples:

* Impact of the invention of the printing press on city growth: quasi-random diffusion of the press from Mainz (Dittmar 2011).

* Impact of the Great Migration of Black Americans northward from US South on agricultural technology: quasi-random variation in intensity of Great Mississippi flood of 1927 (Hornbeck and Naidu 2014).
* Seek historical experiments within pre-specified setting.

* Aim at more compelling causal arguments about process of interest, not testing general economic hypothesis.

* Examples:

* Impact of the invention of the printing press on city growth: quasi-random diffusion of the press from Mainz (Dittmar 2011).

* Impact of the Great Migration of Black Americans northward from US South on agricultural technology: quasi-random variation in intensity of Great Mississippi flood of 1927 (Hornbeck and Naidu 2014).

* Impact of WWI of female labor force participation: variations from cohort-drafting rules (Boehnke and Gay 2020).
* Critique of “looking under the lamppost.”

* Only study component of episode exhibiting useful identifying variation.

* Miss many important historical aspects.
* Critique of “looking under the lamppost.”

* Only study component of episode exhibiting useful identifying variation.

* Miss many important historical aspects.

* Sometimes, amounts to argue for inexplicability of historical variation of interest.

* But understanding source of variation is valuable (even beyond LATE).
* Seek historical source of variation to estimate a parameter.

* The specific setting is not of interest per se.
* Seek historical source of variation to estimate a parameter.

* The specific setting is not of interest per se.

* Examples:

* Causal effect of military service on earnings: use Vietnam War draft lottery as source of variation in draft probability (Angrist 1990).
* Seek historical source of variation to estimate a parameter.

* The specific setting is not of interest per se.

* Examples:

* Causal effect of military service on earnings: use Vietnam War draft lottery as source of variation in draft probability (Angrist 1990).

* Causal effect of arrival of new information on stock prices: use variation in wind delaying boats between England and Amsterdam as a source of variation in arrival of information (Koudijs 2016).
* Seek historical source of variation to estimate a parameter.

* The specific setting is not of interest per se.

* Examples:

* Causal effect of military service on earnings: use Vietnam War draft lottery as source of variation in draft probability (Angrist 1990).

* Causal effect of arrival of new information on stock prices: use variation in wind delaying boats between England and Amsterdam as a source of variation in arrival of information (Koudijs 2016).

* Causal effect of input relative abundance on technical change: use decline in US South cotton imports to Britain during US Civil War as a source of variation in input availability (Hanlon 2015).
* Usually nice “existence” results.

* Sometimes lack of insight on mechanisms.

* Go beyond estimating single parameter.

* Critiques of reduced form à la Heckman (2005) and Deaton (2010).
* Seek historical experiments to identify the causal role of past events (or initial conditions) on the present.

* This is sometimes called the “persistence” literature.
* Seek historical experiments to identify the causal role of past events (or initial conditions) on the present.

* This is sometimes called the “persistence” literature.

* Examples:

* Long-run effects of colonial institutions: AJR (2001, 2002) use settler's mortality as IV for quality of historical institutions. See also Dell (2010), Lowes and Montero (2018), Dell and Olken (2019).
* Seek historical experiments to identify the causal role of past events (or initial conditions) on the present.

* This is sometimes called the “persistence” literature.

* Examples:

* Long-run effects of colonial institutions: AJR (2001, 2002) use settler's mortality as IV for quality of historical institutions. See also Dell (2010), Lowes and Montero (2018), Dell and Olken (2019).

* Long-run effects of slave trade: Nunn (2008) uses distance to major slave-receiving ports as IV for intensity of slavery.
* Seek historical experiments to identify the causal role of past events (or initial conditions) on the present.

* This is sometimes called the “persistence” literature.

* Examples:

* Long-run effects of colonial institutions: AJR (2001, 2002) use settler's mortality as IV for quality of historical institutions. See also Dell (2010), Lowes and Montero (2018), Dell and Olken (2019).

* Long-run effects of slave trade: Nunn (2008) uses distance to major slave-receiving ports as IV for intensity of slavery.

* Long-run effects of disease environment: Alsan (2014) uses suitability for Tze Tze fly as IV for disease environment.
* Causally linking past to present is subject to historical contingency:

* Especially salient when little data around the experiment: challenges to the exclusion restriction.

* A lot of work is needed along the causal chain.
* Causally linking past to present is subject to historical contingency:

* Especially salient when little data around the experiment: challenges to the exclusion restriction.

* A lot of work is needed along the causal chain.

* Spatial correlation when persistence is underestimated (Kelly 2019).
* Causally linking past to present is subject to historical contingency:

* Especially salient when little data around the experiment: challenges to the exclusion restriction.

* A lot of work is needed along the causal chain.

* Spatial correlation when persistence is underestimated (Kelly 2019).

* Reproductibility is challenging, as well as the risk of multiple hypothesis testing.
* Causally linking past to present is subject to historical contingency:

* Especially salient when little data around the experiment: challenges to the exclusion restriction.

* A lot of work is needed along the causal chain.

* Spatial correlation when persistence is underestimated (Kelly 2019).

* Reproductibility is challenging, as well as the risk of multiple hypothesis testing.

* Bias toward persistence. When and why does history not persist?
* Does the past have useful economics?

* A classical approach. McCloskey (1976)

* From classical to modern approach. Arrow (1985) Solow (1985)

* A modern approach. Abramitzky (2015) Cantoni Yuchtman (2020)

* Trends in the field.

* Trends in publishing. Abramitzky (2015) Cioni et al. (2019)

* Historical econometrics. Valencia Caicedo (2021)

* Future trends. Lamoreaux (2015)
## Share of EH articles top-5 general interest

<figure><img src="image_3.png" /></figure>

Source: Abramitzky (2015), Figure 1
<figure><img src="image_4.png" /><figcaption>b T5-EH citations' network 2017</figcaption></figure>

<figure><img src="image_5.png" /></figure>

Citation networks

Source: Cioni et al. (2019), Figure 1
<figure><img src="image_6.png" /><figcaption>EH articles and length</figcaption></figure>

Source: Cioni et al. (2019), Figure 2
Share of historical periods

<figure><img src="image_7.png" /></figure>

Source: Cioni et al. (2019), Figure 3
<figure><img src="image_8.png" /><figcaption>Share of continents</figcaption></figure>

Source: Cioni et al. (2019), Figure 5
## Presence of tables, figures, and econometrics

<figure><img src="image_9.png" /></figure>

Source: Cioni et al. (2019), Figure 6
Valencia Caicedo (2021)

* Natural historical experiment literature increasingly centered on identification.

* Difference-in-differences (parallel counterfactual trends).

* IV (relevance, excludability, monotonicity).

* RDD (smooth characteristics along running variable)

* IV and RDD (even DiD) only recover LATE. Bisin and Moro (2021)
* Three samples 2000–2020 from IDEAS/RePEc:

* EH articles in top-20 general interest journals.

* Articles in top-5 EH journals.

* EH articles in top-5 general interest journals.
<figure><img src="image_10.png" /><figcaption>IV and RDD EH articles in top-20 general interest</figcaption></figure>

Source: Valencia Caicedo (2021), Figure 1
Advanced econometrics articles in top-5 EH

<figure><img src="image_11.png" /></figure>

Advanced econometrics: IV, RDD, DiD, panel, matching

Source: Valencia Caicedo (2021), Figure 2A
Share of advanced econometrics articles in top-5 EH

<figure><img src="image_12.png" /></figure>

Advanced econometrics: IV, RDD, DiD, panel, matching

Source: Valencia Caicedo (2021), Figure 2B
<figure><img src="image_13.png" /><figcaption>Share of standard econometrics in EH articles</figcaption></figure>

Standard econometrics: regression, logit, probit, MLE, coefficient, standard error

Source: Margo (2018), Figure 1
* Seminal articles using historical IV: AJR (2001, 2002).

* Causal impact of institutions on long-run economic development.

* Endogeneity of institutions to income.

* IV for historical institutions: settlers mortality.

* Intensive scrutiny of threats to identification.

* Direct impact of geography through disease environment?

* Selection of settler mortality data?

* Better instrument for human capital?
* Dell (2010)

* Mining *mita* and development in Peru.

* Geographical RDD for presence of *mita*.

* Nunn (2008) and Nunn and Wantchekon (2011)

* Slave trade, development and trust in Africa.

* IV for historical intensity of slave trade: distance to coast.

* Becker and Woessmann (2009)

* Protestantism and economic development in late 19th Prussia.

* IV for prevalence of Protestantism: distance from Wittenberg.

* Dittmar (2011)

* Printing Press and economic development in Europe.

* IV for adoption of printing press: distance from Mainz.
* Seminal articles using historical RDD: Michalopoulos and Papaioannou (2013, 2014, 2016)

* Rely on scramble for Africa: haphazard redrawing of African national boundaries in 1884.

* Random partition of ethnic homelands ⇒ random variation of ethnic composition.

* Explore persistent effects of precolonial ethnic homelands and role of ethnic distribution for conflict.
* Alsan (2015)

* Disease environment and development in Africa.

* IV for disease environment: suitability for tse-tse fly.

* Adena et al (2015)

* Media and support for democratic institutions in Nazi Germany.

* IV for exposure to propaganda: radio signal strength.

* Dell and Querubin (2018)

* Impact of bombing on communist insurgency.

* IV for bombing: rounding thresholds in algorithm used to target air strikes.

* RDD for bombing: military regions with different strategies.
* Pascali (2017)

* Impact of steamship on first globalization (1870–1913).

* IV for differential reduction in speed: wind patterns.

* Juhasz (2018)

* Infant industry argument.

* IV: Napoleonic blockade of UK (1803–1815).

* Becker et al (2020)

* Forced migration and human capital in post-WWII Poland.

* Geographical RDD: relocation of expelled Polish.
* Geography: distance, proximity to rivers, slope of terrain.

* Weather characteristics: wind, rainfall, temperature shocks.

* Land suitability for agriculture.

* Bartik type instruments.

* Forced migrations of ethnic minorities.
* Imperial boundaries: Hasburg Empire, Dai Viet...

* Battelfronts: Gothic line, Aragon Front...

* Pre-colonial boundaries: Kuba Kingdom, matrilineal belt...

* Colonial partitions: Dutch cultivation system, rubber concessions...

* Assignment thresholds: conscription rules, acreage thresholds ...

⇒ See Valencia Caicedo (2021) for refinements to RDD.
* “Big data” in economic history:

* Population data: full count censuses, vital records....

* Automatic linking techniques.
* "Big data" in economic history:

* Population data: full count censuses, vital records....

* Automatic linking techniques.

* Digital humanities:

* Text as data (see Gentzkow and Shapiro's work).

* GIS mappings.

* Ontologies.

⇒ 4D economic history: digitally-driven data design (Mitchener 2015).
* More interdisciplinarity is necessary (Lamoreaux 2015).
* More interdisciplinarity is necessary (Lamoreaux 2015).

* Some applied micro-style economic history lacks ... history, potentially leading to mistakes.

* Interesting answers require deep knowledge of the context and data as constructs.

* In applied micro terms: more history is needed to understand sources of identifying variation, long-run exclusion restrictions, heterogeneity in treatment effects.
* More interdisciplinarity is necessary (Lamoreaux 2015).

* Some applied micro-style economic history lacks ... history, potentially leading to mistakes.

* Interesting answers require deep knowledge of the context and data as constructs.

* In applied micro terms: more history is needed to understand sources of identifying variation, long-run exclusion restrictions, heterogeneity in treatment effects.

> Economic history has never been and should never be anything like a closed filed in which practitioners converse mostly with one another. Instead, it stands at a busy intersection of history and the social sciences, where economists, political scientists, sociologists, anthropologists, demographers, and historians come and go.

Mokyr (2003)
Topic 2
Economics History and Labor Economics
* Female labor and economic development.

* Measuring (female) labor.

* Female labor and wars.
Female labor and economic development
Contemporaneous gender inequality worldwide
<figure><img src="image_14.png" /><figcaption>## UN's Gender Inequality Index (GII)</figcaption></figure>

Source: UNDP (2020)
Distribution of Gender Inequality Index (2019)

<figure><img src="image_15.png" /></figure>

Source: UNDP (2020)
<figure><img src="image_16.png" /><figcaption>Distribution of Gender Inequality Index (2019)</figcaption></figure>

Source: UNDP (2020), 162 countries.
* Female labor force participation (FLFP) rate:

* Share women aged 15 + economically active.

* Share women aged 30-49 economically active.

* Economically active (ILO definition):

* Working for pay or profit.

* Regardless if receive wage.

* Includes unpaid family farming, family business, self-employed.
<figure><img src="image_17.png" /><figcaption>Distribution of FLFP 15+ (2019)</figcaption></figure>

Source: ILO
<figure><img src="image_18.png" /><figcaption>Distribution of FLFP 30-49 (2019)</figcaption></figure>

Source: ILO
<figure><img src="image_19.png" /><figcaption>Distribution of FLFP (2019)</figcaption></figure>

Source: ILO, 189 countries.
<figure><img src="image_20.png" /><figcaption>FLFP and GDP per Capita (2019)</figcaption></figure>

Source: ILO, Penn World Table 10.1, 164 countries.
<figure><img src="image_21.png" /><figcaption>FLFP and GDP per Capita (2019)</figcaption></figure>

Source: ILO, Penn World Table 10.1, 164 countries.
* Relationship between FLFP and development?

* Explore mechanisms behind the U-shape.

* Use a historical perspective throughout the 20th century.
* Relationship between FLFP and development?

* Explore mechanisms behind the U-shape.

* Use a historical perspective throughout the 20th century.

* Why should we care?

* FLFP instrumental to women's empowerment.

* Macro perspective: key input in production function.
* The historical relationship between FLFP and development

* Stylized facts

* The structural transformation

* The 20th century American experience
* The historical relationship between FLFP and development

* Stylized facts

* The structural transformation

* The 20th century American experience

* Revising old views

* A steady progress?

* Measurement issues
Olivetti (2014)

* Unbalanced panel 16 countries, 1890–2005.

* Europe, U.K., U.S., Canada.

* FLFP: share women aged 15 + economically active.

* Economically active (ILO definition):

* Working for pay or profit.

* Regardless if receive wage.

* Includes unpaid family farming, family business, self-employed.
* Pre-WWII data (non-US):

* International Historical Statistics (IHS) from Mitchell (1998).

"Although imperfect, these data are probably as close as possible to being harmonized in terms of the ILO definition of the employment construct. [...] According to Mitchell (1998, 161), the statistics prior to 1968 were unified across different countries and different time periods to adhere to this definition as much as possible." Olivetti (2014, 170-1)
* Pre-WWII data (non-US):

* International Historical Statistics (IHS) from Mitchell (1998).

"Although imperfect, these data are probably as close as possible to being harmonized in terms of the ILO definition of the employment construct. [...] According to Mitchell (1998, 161), the statistics prior to 1968 were unified across different countries and different time periods to adhere to this definition as much as possible." Olivetti (2014, 170-1)

* IHS data from Bairoch et al. (1968).

"The immediate source of all statistics is P. Bairoch et al (1968). The original sources are described in detail there. [...] [They] 'tried to as great an extent as possible to unify the statistics in different countries during different periods', but were unable to achieve anything like perfect comparability." Mitchell (1998, 161)
* Pre-WWII data (US):

* Gainful employment from Goldin (1990, 17).

* "The 1890 figure is artificially low because it undercounts the paid and unpaid work of married women within the home and on the farm." Olivetti (2014, 171)

* Post-WWII data: International Labour Organization (ILO).

⇒ Challenge in recording labor of married women in farming and family businesses.
Trends in FLFP, 1890–2005

<figure><img src="image_22.png" /></figure>

Source: Olivetti (2014, 172).
FLFP and economic development, 1890–2005

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

Source: Olivetti (2014, 174).
<figure><img src="image_24.png" /></figure>

Source: Olivetti (2014, 180).
# The structural transformation

* Structural transformation (Agriculture → Industry → Services):

* Production of goods relatively intensive in use of "brawn."

* Production of services relatively intensive in use of "brain."

* Endowments across gender → comparative advantage.
* Structural transformation (Agriculture → Industry → Services):

* Production of goods relatively intensive in use of "brawn."

* Production of services relatively intensive in use of "brain."

* Endowments across gender ⇒ comparative advantage.

* Decreasing portion of the U:

* Shift from labor to capital intensive agriculture (e.g., plough).

* Initial manufacturing: use of brawn.

* Lack of evidence, mainly due to measurement issues.
* Structural transformation (Agriculture → Industry → Services):

* Production of goods relatively intensive in use of "brawn."

* Production of services relatively intensive in use of "brain."

* Endowments across gender ⇒ comparative advantage.

* Decreasing portion of the U:

* Shift from labor to capital intensive agriculture (e.g., plough).

* Initial manufacturing: use of brawn.

* Lack of evidence, mainly due to measurement issues.

* Increasing portion of the U:

* Expansion of service sector ⇒ ↑ demand for female labor.

* Marketization of home production ⇒ ↑ supply for female labor.
Amplifying factors of increasing part of U Olivetti and Petrongolo (2016)

* Technological change: ↑ time for market + elasticity of labor supply.

* Contraceptive pill. Goldin and Katz (2006) Bailey (2006)

* Substitutes to maternal lactation. Albanesi and Olivetti (2016)

* Household production technologies. Greenwood et al. (2005)

* Availability of child care. Attanasio et al. (2008)
## Amplifying factors of increasing part of U Olivetti and Petrongolo (2016)

* Technological change: ↑ time for market + elasticity of labor supply.

* Contraceptive pill. Goldin and Katz (2006) Bailey (2006)

* Substitutes to maternal lactation. Albanesi and Olivetti (2016)

* Household production technologies. Greenwood et al. (2005)

* Availability of child care. Attanasio et al. (2008)

* Changes in social attitudes toward female labor.

* Changes in husbands' attitudes toward working wives. Fernández et al. (2004)

* Changes in women's beliefs about costs of maternal employment. Fogli and Veldkamp (2011)
Goldin (1991, 2006)

* Phase I (initial): 1890s–1930.

* Phase II (transition): 1930–1950.

* Phase III (roots of revolution): 1950–1970s.

* Phase IV (quiet revolution): post-1970s.
Trends in 20th-century U.S.

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

Source: Goldin (2006, 4).
<figure><img src="image_26.png" /></figure>

Source: Costa (2000, 104).
Trends in 20th-century U.S.

<figure><img src="image_27.png" /></figure>

* FLFP sample: white married women aged 30–49 in nonfarm IPUMS 1900–2016

* Share approves of “A married woman earning money in business or industry if she has a husband capable of supporting her” Gallup 1938 GSS 1972–1998
* Changes in female labor supply depends on:

* Own-wage (compensated) elasticity.

* Income elasticity (esp. husband income).

* Each phase characterized by different parameters.

* Trends can be explained by:

* Changes in elasticities of labor supply.

* Shifts in labor supply.

* Shifts in labor demand.
Phase I: 1890s–1930

<figure><img src="image_28.png" /></figure>

Source: Goldin (2006, 4).
* Trends:

* FLFP married 35–44 increase: 3% to 10%.

* Female workers: young, unmarried, uneducated, poor.

* Sectors: manufacturing, domestic, laundresses.

* Driving forces:

* Strong negative income effect (stigma) ⇒ drop out at marriage.

* Small substitution effect (inflexible schedules).

* Limited shifts in labor supply (↓ fertility, ↑ education)
Phase I: 1890s–1930

<figure><img src="image_29.png" /></figure>
Phase II: 1930–1950

<figure><img src="image_30.png" /></figure>

Source: Goldin (2006, 4).
* Trends:

* FLFP married 35–44 increase: 10% to 25%.

* Female workers: older, married, more educated.

* Sectors: more clerical and office workers.

* Driving forces:

* Weaker income effect (↓ stigma).

* Increased substitution effect (↑ part-time work, ↑ household technology).

* Large shifts of labor supply (↑ education) and demand (↑ offices, ↓ marriage bars).

* Role of WWII?
<figure><img src="image_31.png" /></figure>
Phase III: 1950–1970

<figure><img src="image_32.png" /></figure>

Source: Goldin (2006, 4).
* Trends:
  * FLFP married 35–44 increase: 25% to 46%.

* Female workers: generalized.

* Sectors: services (secretaries, teachers, nurses, social workers...).

* Driving forces:
  * Weaker income effect (↓ stigma).

* Increased substitution effect (↑ part-time work, ↑ household technology).

* Large shifts in demand (↑ services).

⇒ Still jobs, no careers.
Phase III: 1950–1970

<figure><img src="image_33.png" /></figure>
The Quiet Revolution – Phase IV: post-1970s

<figure><img src="image_34.png" /></figure>

Source: Goldin (2006, 4).
* Increases in phases II and III: precondition.

* Increases post-1970 insufficient to characterize revolution.

* Indicators that reflect changes in:

* Horizons.

* Identity.

* Decision making.
<figure><img src="image_35.png" /><figcaption>Employment expectations of female youth by age</figcaption></figure>

Source: Goldin (2006, 9) from NLSY
<figure><img src="image_36.png" /><figcaption>## Female minus male college attendance and graduation rates</figcaption></figure>

Source: Goldin (2006, 10)
<figure><img src="image_37.png" /><figcaption>Median age at first marriage</figcaption></figure>

Source: Goldin (2006, 10)
<figure><img src="image_38.png" /><figcaption>Fraction female among first-year students in professional programs</figcaption></figure>

Source: Goldin (2006, 11)
<figure><img src="image_39.png" /><figcaption>Personal satisfaction factors for college freshmen</figcaption></figure>
Relative earnings and occupations

<figure><img src="image_40.png" /><figcaption>Women's earnings as a percentage of men's earnings</figcaption></figure>

Source: Goldin (2006, 13)
<figure><img src="image_41.png" /><figcaption>Occupations of college graduate women (30–34)</figcaption></figure>

Source: Goldin (2006, 13)
* The meta narrative of a steady convergence maybe unwarranted:

* The "engine of liberation" has stalled in recent years.

* Historical crises oftentimes have backward consequences.

* Women are still most affected by the business cycle.
<figure><img src="image_42.png" /><figcaption>Trends in male and female LFP rates 16+ in the U.S.</figcaption></figure>

Source: Blau and Kahn (2017)
# A stalling engine

<figure><img src="image_43.png" /><figcaption>Female-to-male earnings ratios of full time workers in the U.S.</figcaption></figure>

Source: Blau and Kahn (2017)
Measuring (female) labor
* Women have always worked.

* The way (gender-biased) statistics capture female labor has changed historically.

* Careful thinking of historical measurement might question standard view.
# Measurement issues: economics vs history

* The heterogeneity of sources reflects methodological and epistemological differences across disciplines.

* In economics (and quantitative history):

* Preferred sources: nation-wide coverage, all sectors, temporal recurrence.

* Objective: construct time series and panels for statistical treatment.

* Main sources for this approach: population censuses.
# Measurement issues: economics vs history

* The heterogeneity of sources reflects methodological and epistemological differences across disciplines.

* In economics (and quantitative history):

* Preferred sources: nation-wide coverage, all sectors, temporal recurrence.

* Objective: construct time series and panels for statistical treatment.

* Main sources for this approach: population censuses.

* Criticism from historians of statistics:

* Anachronism of long time series.

* Lack of source criticism, its production context, and representations of its producers.

* Need to deconstruct census data (Schweitzer 2002, Maruani Meron 2012, Genèse 1992).
* In history:

* Preferred sources: micro-analytical scale, centered on specific occupation.

* Objective: uncover female labor less visible in macro sources.

* Main sources for this approach focus on specific occupations:

* Workers: firm archives.

* Nurses: hospital archives.

* Domestics: private archives of bourgeoisie and nobility.

* Teachers: school institutions archives.

* Public servants: public administrations archives.
## Topalov (1999)

* Categories of active populations emerge and change late 19th-early 20th century as formal markets and waged labor develop.

* Constructed reality (formal, informal) dependent on norms (e.g., occupation's wife).

* Difficult issues to solve for census enumerators:

* How much “labor” to be “active”? Should it be paid?

* Account for individual activity or within production unit?

* Domestic labor and gainful employment vs “housewife.”

* Unoccupied classes (retired, landlords, students, ...).

* Unemployed and looking for work.
* France:

* Active-inactive distinction: 1861.

* Notion: "profession, position ou occupation" (1851–1901) then "profession".

* U.S.:

* Active-inactive distinction: 1870.

* Notion: "profession, occupation or trade" (1820–1900), "trade or profession" (1910), then "occupation".

* U.K.:

* Active-inactive distinction: 1881.

* Notion: "rank, profession, or occupation" (1801–1881), "profession or occupation" (1891), then "personal occupation".
* The emergence of the notion of active population:

* 1836-1856: occupation of head of household attributed to entire household.

* 1861-1891: active population = heads of households + householders declaring a distinct occupation. Others + domestics = inactive population.

* 1896: individualization of the notion of gainful employment
* Still, notion takes time to stabilize:

* After 1896: gainful employment (paid domestic vs "housewife").

* After 1906: wives of farm owners counted as active.

* Wives of shopkeepers not all counted as active.

* Changes in counting techniques matter:

* Starting 1901: centralization in Paris of census counting operations with punched card machines.

* Starting 1906: cross-tabulations between individual and establishment bulletins.

⇒ Different trends depending on definition.
* FLFP 15+ uncorrected census data. Olivetti (2014, 2016)

* FLFP 15+ non-farming. Daric (1947) Maruani and Méron (2012)

* FLFP 15+ estimated based on 1954 definition and constant ratio male-female in agriculture by age group in 1954. Marchand and Thello (1998) Costa (2000)

* FLFP 15+ net female farm owners. Beohnke and Gay (2022)
<figure><img src="image_44.png" /><figcaption>Female labor force participation, 15+</figcaption></figure>
<figure><img src="image_45.png" /><figcaption>Female labor force participation, 15+</figcaption></figure>
# Measuring female labor in British censuses

## You (2020)

* Measure of married women's labor problematic.

* "Occupation's wife" is largest group in British census 1851–1911.

* Compare information in census reports and (100%) Census Enumerators' Books.

* Before 1881: wives of farmers, shopkeepers, inkeepers, butchers assumed to work with husbands in same trade unless explicitly declare other occupation.

* Unclear how census enumerators followed instructions.

* Shows enumerator-level idiosyncrasies (local adherence to norms).
<figure><img src="image_46.png" /><figcaption>Spatial pattern of married women's LFP, 1881</figcaption></figure>

Source: You (2020, 12)
Chiswick and Robinson (2021)

* Labor contributions by women to family-operated businesses heavily discounted in official statistics.

* Correcting for farm, merchant, craft family workers, and boarding house keepers dramatically changes FLFP estimates 1860–1920.

* Overall change might be due to shift in unreported work in family businesses to reported work in paid labor market.
Augmented Labor Force Participation Rates, 16+

<table><thead><tr><th rowspan="2">Formal or Informal Occupation</th><th colspan="2">1860 – All</th><th colspan="2">1920 – All</th></tr><tr><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td>Gainful Occupation</td><td>87.6</td><td>15.7</td><td>89.9</td><td>24.0</td></tr><tr><td>All Family Workers</td><td>5.6</td><td>40.9</td><td>2.9</td><td>25.8</td></tr><tr><td>Farm Family Workers</td><td>5.1</td><td>35.8</td><td>2.3</td><td>19.9</td></tr><tr><td>Merchant Family Workers</td><td>0.3</td><td>3.0</td><td>0.4</td><td>3.6</td></tr><tr><td>Craft Family Workers</td><td>0.1</td><td>0.8</td><td>0.1</td><td>1.5</td></tr><tr><td>Boardinghouse Family Workers</td><td>0.1</td><td>0.8</td><td>0.1</td><td>0.5</td></tr><tr><td>Multi-Job Family Workers</td><td>0.1</td><td>0.5</td><td>0.0</td><td>0.3</td></tr><tr><td>Total Augmented</td><td>93.2</td><td>56.6</td><td>92.8</td><td>49.8</td></tr><tr><td>Total Not Working</td><td>6.8</td><td>43.4</td><td>7.2</td><td>50.2</td></tr><tr><td>Total Adult Population</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr><tr><td>Sample Size</td><td>81,657</td><td>76,605</td><td>357,236</td><td>339,181</td></tr></tbody></table>

Source: Chiswick and Robinson (2021)
* Anachronism of long-run time series?

* Long-run analyses can make unwarranted comparisons over time, assuming the existence of the object (does dichotomy active / inactive make sense?).

* Be clear about joint measure and construction of object (historicize measure).

* Comparisons when measure stabilized within short time-frame (though interesting phenomena across changing conditions).
Female labor and wars
# Why female labor and wars?

* Substantial historical importance (cat. 1).

* Wars of the 20th century were among most dramatic events.

* Gender relations dramatically altered by industrial warfare.

* Critical junctures for social change.
# Why female labor and wars?

* Substantial historical importance (cat. 1).

* Wars of the 20th century were among most dramatic events.

* Gender relations dramatically altered by industrial warfare.

* Critical junctures for social change.

* General economic insights (cat. 2).

* Test economic theory (sex ratio, marriage and labor market). Abramistky et al (2011) Boehnke and Gay (2020)

* Learn about implications of Revolution of female labor: source of exogenous variation in female labor supply. Acemoglu et al. (2004)

* Learn about mechanisms of intergenerational transmission: source of exogenous variation in former generation female labor supply. Fernandez, Fogli, and Olivetti (2004) Gay (2021)
# Why female labor and wars?

* Substantial historical importance (cat. 1).

* Wars of the 20th century were among most dramatic events.

* Gender relations dramatically altered by industrial warfare.

* Critical junctures for social change.

* General economic insights (cat. 2).

* Test economic theory (sex ratio, marriage and labor market). Abramistky et al (2011) Boehnke and Gay (2020)

* Learn about implications of Revolution of female labor: source of exogenous variation in female labor supply. Acemoglu et al. (2004)

* Learn about mechanisms of intergenerational transmission: source of exogenous variation in former generation female labor supply. Fernandez, Fogli, and Olivetti (2004) Gay (2021)

* Roots of contemporary gendered structure of labor markets (cat. 3).
* Female labor and WWII in the U.S.

* Female labor and WWI in France.
Female Labor and WWII in the U.S.
* Goldin (1991)

* Acemoglu, Autor, and Lyle (2004)

* Goldin and Olivetti (2013)

* Fernandez, Fogli, and Olivetti (2004)

* Rose (2018)
<figure><img src="image_47.png" /><figcaption>LFP of white married women</figcaption></figure>

Source: Goldin (1991), Figure 1
# WWII: a watershed event?

* Supply side during and retention after:

* Mobilized husband ⇒ ↑ time + ↓ income.

* Patriotic duty to join war effort.

* Job training during wartime employment.

* Changes in husbands' norms.
# WWII: a watershed event?

* Supply side during and retention after:

* Mobilized husband ⇒ ↑ time + ↓ income.

* Patriotic duty to join war effort.

* Job training during wartime employment.

* Changes in husbands' norms.

* Demand side during and retention after:

* Increased demand and wages for wartime industries.

* Updated beliefs about women's ability by employers.

* Erosion of marriage bars.
# WWII: a watershed event?

* Supply side during and retention after:

* Mobilized husband ⇒ ↑ time + ↓ income.

* Patriotic duty to join war effort.

* Job training during wartime employment.

* Changes in husbands' norms.

* Demand side during and retention after:

* Increased demand and wages for wartime industries.

* Updated beliefs about women's ability by employers.

* Erosion of marriage bars.

* But wartime jobs disappeared after the war (and in industrial sector).
* Retrospective work histories survey (Palmer Survey):

* 4,350 women.

* Working full-time for at least a month in 1950.

* Urban sample across the U.S.

* Labor status: Jan-1940, Dec-1944, Dec-1949, Jan-1951.

* Rich information (industry, earnings, education, marital status...)
* Research questions:

* Did women who entered the labor force during WWII remained employed in 1950?

* Did these women left the labor force, and the post-WWII rise came from a post-war movement of women into the labor force?
* Research questions:

* Did women who entered the labor force during WWII remained employed in 1950?

* Did these women left the labor force, and the post-WWII rise came from a post-war movement of women into the labor force?

* Results among white married women aged 35–64 employed in 1950:

* 52% already employed in 1940.

* 22% joined labor force during the war.

* 25% joined the labor force after the war.
* Research questions:

* Did women who entered the labor force during WWII remained employed in 1950?

* Did these women left the labor force, and the post-WWII rise came from a post-war movement of women into the labor force?

* Results among white married women aged 35-64 employed in 1950:

* 52% already employed in 1940.

* 22% joined labor force during the war.

* 25% joined the labor force after the war.

* Results linking with CPS 1944 (with retrospective questions): half of wartime entrants exited labor force by 1951.

⇒ Relatively modest implications based on selected sample.
LFP of white married women

<table><thead><tr><th rowspan="2">Category</th><th colspan="3">Participation rate (percentage)</th></tr><tr><th>Age 35-64<br>in 1950</th><th>Age 35-44<br>in 1950</th><th>Age 45-54<br>in 1950</th></tr></thead><tbody><tr><td colspan="4">In labor force in 1950 and in labor force in:</td></tr><tr><td>1940 and 1944</td><td>45.5</td><td>41.7</td><td>48.1</td></tr><tr><td>1940 but not 1944</td><td>6.6</td><td>8.2</td><td>5.3</td></tr><tr><td>1944 but not 1940</td><td>21.8</td><td>20.9</td><td>22.8</td></tr><tr><td>Neither 1940 nor 1944</td><td>26.2</td><td>29.1</td><td>23.7</td></tr><tr><td>Number of observations:</td><td>1,199</td><td>635</td><td>451</td></tr><tr><td colspan="4">In labor force in January 1951 and in labor force in:</td></tr><tr><td>1940 and 1944</td><td>47.7</td><td>44.7</td><td>49.8</td></tr><tr><td>1940 but not 1944</td><td>6.3</td><td>7.5</td><td>5.4</td></tr><tr><td>1944 but not 1940</td><td>21.0</td><td>19.9</td><td>22.3</td></tr><tr><td>Neither 1940 nor 1944</td><td>25.0</td><td>27.7</td><td>22.5</td></tr><tr><td>Number of observations:</td><td>1,067</td><td>558</td><td>404</td></tr></tbody></table>
* How did increased FLFP affect structure of male and female wages?

* Use WWII as exogenous source of variation in female labor supply.

* Use state-level male mobilization to capture pull force to female labor during the war.
<figure><img src="image_48.png" /><figcaption>MLFP, FLFP, military active service personnel, 1940–1952</figcaption></figure>

Source: Acemoglu et al. (2004), Figure 2
• Labor data:

* Decennial censuses 1940–1950 (1% extracts).

* Sample: women aged 14–64 with earnings information, not in institutional group quarters, not employed in farming.

* Measure of FLFP: weeks worked.

• Mobilization data:

* Mandatory draft through 6 waves 1940–1947, men 18–44.

* Many deferrals for farmers and little nonwhite units.

* Use state share of enlisted men aged 13–44 in 1940 to proxy for decline in supply of male labor (range from 43 to 53%; sd = 3.2).
# Impact of WWII in the U.S. on FLFP?

WWII mobilization rates and mean female weeks worked per year, 1940

<figure><img src="image_49.png" /></figure>

Source: Acemoglu et al. (2004), Figure 3
# Impact of WWII in the U.S. on FLFP?

WWII mobilization rates and change in female weeks worked per year, 1940–1950

<figure><img src="image_50.png" /></figure>

Source: Acemoglu et al. (2004), Figure 4
# Impact of WWII in the U.S. on FLFP?

WWII mobilization rates and change in female weeks worked per year, 1950–1960

<figure><img src="image_51.png" /></figure>

Source: Acemoglu et al. (2004), Figure 5
1940 state-level determinants of WWII mobilization rates

<table><thead><tr><th rowspan="2"></th><th rowspan="2">MEAN</th><th colspan="10">REGRESSION</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Share farmers</td><td>.15<br>[.11]</td><td>-.15<br>(.05)</td><td>-.16<br>(.04)</td><td>-.17<br>(.03)</td><td>-.17<br>(.04)</td><td>-.23<br>(.06)</td><td>-.26<br>(.04)</td><td>-.22<br>(.04)</td><td>-.17<br>(.05)</td><td>-.16<br>(.04)</td><td>-.17<br>(.05)</td></tr><tr><td>Share nonwhite</td><td>.10<br>[.11]</td><td>-.01<br>(.05)</td><td>-.07<br>(.04)</td><td></td><td>-.03<br>(.06)</td><td>-.38<br>(.27)</td><td>.04<br>(.05)</td><td>-.03<br>(.05)</td><td>-.03<br>(.06)</td><td>.02<br>(.06)</td><td>-.03<br>(.06)</td></tr><tr><td>Average education</td><td>.89<br>[.71]</td><td>.02<br>(.01)</td><td></td><td>.01<br>(.01)</td><td>.01<br>(.01)</td><td>.01<br>(.01)</td><td>.03<br>(.01)</td><td>.01<br>(.01)</td><td>.01<br>(.01)</td><td>.01<br>(.01)</td><td>.01<br>(.01)</td></tr><tr><td>Share aged 13-24</td><td>.42<br>[.03]</td><td>.25<br>(.34)</td><td></td><td></td><td></td><td>.73<br>(.24)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Share aged 25-34</td><td>.31<br>[.01]</td><td>.15<br>(.48)</td><td></td><td></td><td></td><td>.38<br>(.48)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Share German</td><td>.007<br>[.006]</td><td>-3.19<br>(.89)</td><td></td><td></td><td></td><td></td><td>-1.88<br>(.55)</td><td></td><td></td><td></td><td></td></tr><tr><td>Share Italian or Japanese</td><td>.010<br>[.012]</td><td>1.70<br>(.52)</td><td></td><td></td><td></td><td></td><td></td><td>.00<br>(.42)</td><td></td><td></td><td></td></tr><tr><td>Share married</td><td>.50<br>[.03]</td><td>-.10<br>(.17)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-.22<br>(.13)</td><td></td><td></td></tr><tr><td>Share fathers</td><td>.47<br>[.03]</td><td>.08<br>(.13)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.00<br>(.12)</td><td></td></tr><tr><td>R<sup>2</sup></td><td></td><td>.78</td><td>.57</td><td>.58</td><td>.58</td><td>.39</td><td>.68</td><td>.67</td><td>.58</td><td>.61</td><td>.58</td></tr><tr><td>Southern states</td><td></td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>no</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr></tbody></table>

Source: Acemoglu et al. (2004), Table 4
$$y_{ist} = \delta_s + \gamma d_{1950} + X'_{ist}\beta_t + \psi d_{1950}m_s + \varepsilon_{ist}$$

* $y_{ist}$: weeks work by women i residing in state s in year t.

* $\delta_s$: state of residence FE.

* $X_{ist}$: covariates (state of birth FE, age, race, state share farmers and nonwhites, average schooling in 1940, interacted with 1950 dummy).

* $m_s$: state mobilization rate.
## Impact of WWII mobilization rates on female labor supply

<table><thead><tr><th></th><th colspan="4">REGRESSION</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th colspan="4">A. White Females (N=530,026)</th></tr></thead><tbody><tr><td>Mobilization rate x 1950</td><td>11.17<br>(1.89)</td><td>9.85<br>(2.05)</td><td>10.64<br>(2.65)</td><td>8.51<br>(2.37)</td></tr><tr><td>1940 male share farmers x 1950</td><td></td><td></td><td>1.74<br>(1.08)</td><td>1.04<br>(1.05)</td></tr><tr><td>1940 male share nonwhite x 1950</td><td></td><td></td><td>-1.96<br>(1.15)</td><td>-.72<br>(1.37)</td></tr><tr><td>1940 male share average education x 1950</td><td></td><td></td><td></td><td>.52<br>(.16)</td></tr><tr><td>R<sup>2</sup></td><td>.01</td><td>.18</td><td>.18</td><td>.18</td></tr><tr><td>Includes marital status, age, state of birth</td><td>no</td><td>yes</td><td>yes</td><td>yes</td></tr></tbody></table>

* ↑ 10pp mobilization ⇒ ↑ 0.85 week (0.5 sd).
# Baseline results

## Impact of WWII mobilization rates on female labor supply

<table><thead><tr><th rowspan="2"></th><th colspan="2">1940-50</th><th colspan="2">1950-60</th></tr><tr><th>Weeks Worked (1)</th><th>Any Weeks Worked (2)</th><th>Weeks Worked (3)</th><th>Any Weeks Worked (4)</th></tr></thead><tbody><tr><td>A. Baseline specification</td><td>9.85 (2.05)</td><td>.063 (.069)</td><td>-7.25 (1.81)</td><td>-.057 (.049)</td></tr><tr><td>R<sup>2</sup></td><td>.18</td><td>.18</td><td>.15</td><td>.14</td></tr><tr><td>Observations</td><td colspan="2">530,026</td><td colspan="2">615,590</td></tr><tr><td>B. Includes share farmers, share nonwhite, average education</td><td>8.51 (2.37)</td><td>.174 (.071)</td><td>2.15 (1.95)</td><td>-.006 (.067)</td></tr><tr><td>R<sup>2</sup></td><td>.18</td><td>.18</td><td>.15</td><td>.14</td></tr><tr><td>Observations</td><td colspan="2">530,026</td><td colspan="2">615,590</td></tr></tbody></table>

↑ 10pp mobilization ⇒ ↑ 2pp in FLFP (any weeks worked in year).
* Identify short and long-run impacts of WWII on women's labor supply.

* Focus on young white women aged 16–29 during the war (1941–1945): 25–34 in 1950, 35–44 in 1960.

* Assess differential treatment of the war across groups (age, education, children, marital status).

* Use state-level mobilization rates of men aged 18–44 (ranges from 40 to 54%).

* Data from censuses 1940–1960.

* Sample of white married women with $\ge$ 12 years schooling, not in institutional group quarters, not employed in farming, born and residing in U.S.
$$y_{ist} = \psi_s + \gamma d_{\tau} + \alpha_t Z_{it} + \beta_t d_{\tau} X_{s1940} + \delta d_{\tau} MOB_s + \varepsilon_{ist}$$

* $y_{ist}$: weeks work or LFP by women i residing in state s in year t.

* $\psi_s$: state of birth FE.

* $d_{\tau}$: year indicator (1950 or 1960).

* $Z_{it}$: covariates (number of children and age indicators).

* $X_{s1940}$: state of birth covariates (share farmers, nonwhite, average education).

* $MOB_s$: state mobilization rate.

* Control group: same characteristics in 1940.
# Baseline results

Impact of WWII mobilization on labor supply of educated white married women

<table><thead><tr><th rowspan="2"></th><th colspan="2">25-34 years old in 1950</th><th colspan="2">35-44 years old in 1960</th></tr><tr><th>Weeks worked<br>(1)</th><th>LFPR<br>(2)</th><th>Weeks worked<br>(3)</th><th>LFPR<br>(4)</th></tr></thead><tbody><tr><td>τ × MOB × MarrChildWWII</td><td>23.65<br>(15.48)</td><td>0.90***<br>(0.3)</td><td>42.68**<br>(17.86)</td><td>0.68*<br>(0.41)</td></tr><tr><td>τ × MOB × MarrNoChildWWII</td><td>43.33**<br>(17.14)</td><td>0.96***<br>(0.34)</td><td>31.89***<br>(12.06)</td><td>0.64**<br>(0.31)</td></tr><tr><td>τ × MOB × UnmarrWWII</td><td>37.95*<br>(19.48)</td><td>0.79<br>(0.49)</td><td>7.61<br>(15.11)</td><td>0.1<br>(0.35)</td></tr><tr><td>Constant</td><td>18.15***<br>(0.88)</td><td>0.39***<br>(0.02)</td><td>13.19***<br>(1.01)</td><td>0.30***<br>(0.02)</td></tr><tr><td>Observations</td><td>20,846</td><td>20,846</td><td>49,009</td><td>49,009</td></tr><tr><td>R<sup>2</sup></td><td>0.16</td><td>0.14</td><td>0.11</td><td>0.11</td></tr></tbody></table>

* No effect on less educated and nonwhite women.

* WWII pulled less educated in blue collar occupations, which disappeared after the war.
* Role of husbands' norms for the Revolution of female labor?

* Theoretical mechanism:

* Growing number of men who grew up with working mother ⇒ ↑ preference toward working wife and/or ↑ productivity in household production.

* Working becomes more valuable trait in the marriage market ⇒ women invest in HK and have higher wage draws when adult.
* Role of husbands' norms for the Revolution of female labor?

* Theoretical mechanism:

* Growing number of men who grew up with working mother ⇒ ↑ preference toward working wife and/or ↑ productivity in household production.

* Working becomes more valuable trait in the marriage market ⇒ women invest in HK and have higher wage draws when adult.

* Use WWII in U.S. as source of exogenous variation in labor supply of mothers-in-laws of 1930–1935 cohorts (too young to be directly affected by the war)
* Wartime mobilization: measure from Acemoglu et al (2004).

* Data: 1% decennial censuses 1940–1980.

* Sample: white married women outside farming and group quarters.

* Measure of female labor: weeks worked.
$$w_{ist} = X'_{ist}\beta_1 + \gamma_t\tilde{X}'_{ist}\beta_{2t} + \gamma_t e'_s\beta_{3t} + \alpha_t\gamma_t m_s + d_s + \gamma_t + \varepsilon_{ist}$$

* $w_{ist}$: weeks worked by woman i born in state s at time t.

* $X'_{ist}$: individual controls (age, state of residence, husband state of birth indicators).

* $\tilde{X}'_{ist}$: age indicators interacted with year FE $\gamma_t$ effects after 1940.

* $d_s$: state FE.

* $e'_s$: state characteristics interacted with year indicator after 1940.
Impact of WWII on labor supply of 42–47 married women

<table><thead><tr><th colspan="2">Dependent variable is "Weeks Worked"</th></tr><tr><th></th><th>1940 & 1950</th></tr></thead><tbody><tr><td>1940 mobilization rate × year</td><td>19.64**<br>(8.71)</td></tr><tr><td>1940 share male nonwhite × year</td><td>-2.37<br>(4.66)</td></tr><tr><td>1940 share male farmer × year</td><td>6.12***<br>(1.79)</td></tr><tr><td>1940 male avg years educ × year</td><td>.218<br>(.483)</td></tr><tr><td>Year</td><td>-6.80<br>(6.01)</td></tr><tr><td>N. obs.</td><td>32,347</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.03</td></tr></tbody></table>

* Mothers (1903–1908) of cohort of interest (1930–1935).

* 42–47 in 1950 (keep same group in 1940)
Impact of WWII on labor supply of married women

<table><caption>Dependent variable is "Weeks Worked"<br>Panel A: 25-30</caption><thead><tr><th></th><th>(i)</th><th>(ii)</th><th>(iii)</th><th>(iv)</th></tr></thead><tbody><tr><td>1940 mobilization rate × 1950</td><td>18.11<br>(11.05)</td><td>17.29<br>(10.99)</td><td>21.68<br>(14.50)</td><td>22.59<br>(14.24)</td></tr><tr><td>1940 mobilization rate × 1960</td><td>22.71**<br>(11.30)</td><td>19.06*<br>(11.12)</td><td>26.68*<br>(15.1)</td><td>26.39*<br>(15.15)</td></tr><tr><td>Year 1950</td><td>-11.23*<br>(6.26)</td><td>-11.49*<br>(6.16)</td><td>-19.44**<br>(7.51)</td><td>-17.06**<br>(8.19)</td></tr><tr><td>Year 1960</td><td>-5.58<br>(5.89)</td><td>-3.24<br>(5.72)</td><td>-11.73*<br>(7.02)</td><td>-14.54*<br>(7.47)</td></tr><tr><td>St. of residence &amp; husband's<br>st. of birth</td><td></td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Education</td><td></td><td></td><td></td><td>yes</td></tr><tr><td>N. obs.</td><td>75,748</td><td>73,710</td><td>50,146</td><td>50,146</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.01</td><td>0.015</td><td>0.016</td><td>0.027</td></tr></tbody></table>
Impact of WWII on labor supply of married women

<table><caption>Panel B: 35-40</caption><thead><tr><th></th><th>(i)</th><th>(ii)</th><th>(iii)</th><th>(iv)</th></tr></thead><tbody><tr><td>1940 mobilization rate × 1950</td><td>25.25***<br>(8.36)</td><td>23.67***<br>(7.92)</td><td>33.02***<br>(9.99)</td><td>34.49***<br>(10.28)</td></tr><tr><td>1940 mobilization rate × 1960</td><td>18.34***<br>(6.76)</td><td>18.17***<br>(6.29)</td><td>22.55***<br>(7.89)</td><td>24.74***<br>(8.22)</td></tr><tr><td>1940 mobilization rate × 1970</td><td>14.24*<br>(8.07)</td><td>14.78**<br>(7.51)</td><td>22.01**<br>(8.74)</td><td>25.12***<br>(8.64)</td></tr><tr><td>Year 1950</td><td>-2.25<br>(5.87)</td><td>-1.12<br>(5.63)</td><td>-11.88<br>(7.79)</td><td>-20.55***<br>(7.90)</td></tr><tr><td>Year 1960</td><td>-3.76<br>(4.91)</td><td>-3.12<br>(4.75)</td><td>-7.30<br>(6.73)</td><td>-16.73**<br>(6.81)</td></tr><tr><td>Year 1970</td><td>1.79<br>(6.02)</td><td>.99<br>(5.65)</td><td>-6.19<br>(7.87)</td><td>-7.58<br>(9.02)</td></tr><tr><td>St. of residence &amp; husband's<br>st. of birth</td><td></td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Education</td><td></td><td></td><td></td><td>yes</td></tr><tr><td>N. obs.</td><td>112,125</td><td>109,864</td><td>71,018</td><td>71,018</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.039</td><td>0.045</td><td>0.05</td><td>0.05</td></tr></tbody></table>
Impact of WWII on labor supply of married women

<table><caption>Panel C: 45-50</caption><thead><tr><th></th><th>(i)</th><th>(ii)</th><th>(iii)</th><th>(iv)</th></tr></thead><tbody><tr><td>1940 mobilization rate × 1950</td><td>12.59<br>(11.39)</td><td>17.92<br>(11.59)</td><td>27.98*<br>(14.79)</td><td>26.17*<br>(14.79)</td></tr><tr><td>1940 mobilization rate × 1960</td><td>11.47<br>(9.24)</td><td>16.22*<br>(9.29)</td><td>15.44<br>(12.54)</td><td>15.33<br>(12.59)</td></tr><tr><td>1940 mobilization rate × 1970</td><td>3.25<br>(8.56)</td><td>8.99<br>(8.75)</td><td>13.77<br>(12.31)</td><td>15.96<br>(12.23)</td></tr><tr><td>1940 mobilization rate × 1980</td><td>17.26*<br>(10.18)</td><td>21.72**<br>(10.76)</td><td>32.89**<br>(14.78)</td><td>33.07**<br>(14.78)</td></tr><tr><td>Year 1950</td><td>-14.55<br>(9.59)</td><td>-17.96**<br>(9.44)</td><td>-27.31**<br>(13.21)</td><td>-26.23**<br>(13.08)</td></tr><tr><td>Year 1960</td><td>.52<br>(6.53)</td><td>-2.09<br>(6.44)</td><td>-7.95<br>(10.39)</td><td>-11.71<br>(10.81)</td></tr><tr><td>Year 1970</td><td>9.50<br>(6.84)</td><td>5.07<br>(6.79)</td><td>1.82<br>(10.27)</td><td>-8.88<br>(10.59)</td></tr><tr><td>Year 1980</td><td>8.18<br>(7.46)</td><td>5.01<br>(7.60)</td><td>-1.84<br>(11.39)</td><td>-7.01<br>(11.80)</td></tr><tr><td>St. of residence &amp; husband's<br>st. of birth</td><td></td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Education</td><td></td><td></td><td></td><td>yes</td></tr><tr><td>N. obs.</td><td>129,899</td><td>126,715</td><td>80,261</td><td>80,261</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.087</td><td>0.091</td><td>0.098</td><td>0.11</td></tr></tbody></table>
Impact of WWII on labor supply of married women

<table><thead><tr><th colspan="5">Dependent variable is "Weeks Worked"</th></tr><tr><th></th><th>1940 & 1950</th><th>1950 & 1960</th><th>1960 & 1970</th><th>1970 & 1980</th></tr></thead><tbody><tr><td>1940 mobilization rate ×<br>year</td><td>16.48**<br>(7.87)</td><td>-11.66<br>(8.46)</td><td>-2.10<br>(6.28)</td><td>20.65***<br>(7.07)</td></tr><tr><td>1940 share male nonwhite ×<br>year</td><td>4.56<br>(4.91)</td><td>-9.00*<br>(4.78)</td><td>-1.77<br>(4.54)</td><td>-1.81<br>(2.43)</td></tr><tr><td>1940 share male farmer ×<br>year</td><td>9.84***<br>(2.03)</td><td>-4.39*<br>(2.42)</td><td>2.23<br>(1.86)</td><td>2.96<br>(2.55)</td></tr><tr><td>1940 male avg years educ ×<br>year</td><td>2.12***<br>(.436)</td><td>-9.6**<br>(.476)</td><td>-46<br>(.576)</td><td>-21<br>(.225)</td></tr><tr><td>Year</td><td>-22.35**<br>(6.12)</td><td>20.93***<br>(7.62)</td><td>10.01<br>(6.33)</td><td>-4.37<br>(4.69)</td></tr><tr><td>N. obs.</td><td>15,955</td><td>25,127</td><td>45,015</td><td>45,037</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.035</td><td>.029</td><td>.015</td><td>.017</td></tr></tbody></table>
* Assess the impact of WWII female wartime employment directly.

* Data on female wartime employment at State Economic Areas level (grouping of counties within states).

* Data on state-level manpower mobilization (same as AAL 2004 and GO 2013), but also induction records and war deaths aggregated at the SEA level, normalized by the 1940 population aged 21 to 54.

* Data on industrial mobilization with county-level military contracts spending.
* Assess the impact of WWII female wartime employment directly.

* Data on female wartime employment at State Economic Areas level (grouping of counties within states).

* Data on state-level manpower mobilization (same as AAL 2004 and GO 2013), but also induction records and war deaths aggregated at the SEA level, normalized by the 1940 population aged 21 to 54.

* Data on industrial mobilization with county-level military contracts spending.

* Female employment growth 1940–1944 linked to military contracts growth but unrelated to manpower mobilization.
# Female wartime employment and war contracts

Dependent variable: change in female employment (1940–1944), SEA level

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>War contracts per capita</td><td>0.50***<br>(0.04)</td><td>0.33***<br>(0.04)</td><td></td></tr><tr><td>Inductions</td><td></td><td></td><td>-0.02<br>(0.03)</td></tr><tr><td>War deaths</td><td></td><td></td><td></td></tr><tr><td>1940 share emp. manufacturing</td><td></td><td>-0.30<br>(0.59)</td><td></td></tr><tr><td>1940 share pop white</td><td></td><td>1.44***<br>(0.35)</td><td></td></tr><tr><td>1940 median school women 25+</td><td></td><td>0.03<br>(0.03)</td><td></td></tr><tr><td>1940 female LFP rate</td><td></td><td>0.05***<br>(0.01)</td><td></td></tr><tr><td>R2</td><td>0.29</td><td>0.41</td><td>0.00</td></tr><tr><td>N</td><td>407</td><td>407</td><td>407</td></tr></tbody></table>

* All variable standardized.
# Female wartime employment and war contracts

Dependent variable: change in female employment (1940–1944), state level

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>War contracts per capita</td><td>0.27**<br>(0.08)</td><td>0.24*<br>(0.10)</td><td></td><td></td></tr><tr><td>Inductions</td><td></td><td></td><td>0.15<br>(0.09)</td><td>0.01<br>(0.14)</td></tr><tr><td>War deaths</td><td></td><td></td><td></td><td></td></tr><tr><td>1940 share emp. manufacturing</td><td></td><td>1.59<br>(1.91)</td><td></td><td>3.16<br>(2.16)</td></tr><tr><td>1940 share pop white</td><td></td><td>-0.39<br>(1.09)</td><td></td><td>-0.28<br>(1.17)</td></tr><tr><td>1940 median school women 25+</td><td></td><td>0.11<br>(0.15)</td><td></td><td>0.20<br>(0.18)</td></tr><tr><td>1940 female LFP rate</td><td></td><td>-3.21<br>(2.05)</td><td></td><td>-3.06<br>(2.21)</td></tr><tr><td>R2</td><td>0.17</td><td>0.26</td><td>0.06</td><td>0.15</td></tr><tr><td>N</td><td>49</td><td>49</td><td>49</td><td>49</td></tr></tbody></table>

* All variable standardized.
$$Y_{ist} = \alpha_s + \beta_0 d_{1950} + \beta_1 WMC_s d_{1950} + \beta_2 X_{ist} + \beta_3 X_{ist} d_{1950} + \varepsilon_{ist}$$

* $Y_{ist}$: LFP and hours worked by women i residing in SEA s in year t (1940 or 1950).

* $\alpha_s$: SEA FE.

* $WMC_s$: female wartime employment growth 1940–1944.

* $X_{ist}$: covariates (state of birth FE, age, race, SEA share farmers and nonwhites, average schooling in 1940, interacted with 1950 dummy).

* Sample: census 1940 and 1950, women aged 14 to 64 not working in farming.
Impact of wartime work on FLFP in 1950

<table><thead><tr><th rowspan="2"></th><th colspan="3">LFP</th><th colspan="3">Hours Worked</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 colspan="7"><strong>A. All Women</strong></td></tr><tr><td>WMC female emp. intensity x 1950</td><td>-0.002<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>0.003<br>(0.002)</td><td>-0.080<br>(0.067)</td><td>-0.048<br>(0.067)</td><td>0.032<br>(0.064)</td></tr><tr><td>Share emp. farm x 1950</td><td></td><td></td><td>0.075***<br>(0.020)</td><td></td><td></td><td>1.868**<br>(0.604)</td></tr><tr><td>Share pop white x 1950</td><td></td><td></td><td>0.021<br>(0.012)</td><td></td><td></td><td>1.524***<br>(0.385)</td></tr><tr><td>Med. sch. women x 1950</td><td></td><td></td><td>0.006**<br>(0.002)</td><td></td><td></td><td>0.101*<br>(0.046)</td></tr><tr><td>Birth place, age, marital dummies</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>R2</td><td>0.03</td><td>0.25</td><td>0.25</td><td>0.02</td><td>0.20</td><td>0.20</td></tr><tr><td>N</td><td>1,031,743</td><td>1,031,743</td><td>1,031,743</td><td>1,031,743</td><td>1,031,743</td><td>1,031,743</td></tr><tr><td colspan="7"><strong>B. White Women</strong></td></tr><tr><td>WMC female emp. intensity x 1950</td><td>-0.003<br>(0.002)</td><td>-0.002<br>(0.002)</td><td>0.004*<br>(0.002)</td><td>-0.165*<br>(0.079)</td><td>-0.150<br>(0.077)</td><td>0.045<br>(0.071)</td></tr><tr><td>Share emp. farm x 1950</td><td></td><td></td><td>0.081***<br>(0.023)</td><td></td><td></td><td>2.068**<br>(0.682)</td></tr><tr><td>Share pop white x 1950</td><td></td><td></td><td>-0.042**<br>(0.013)</td><td></td><td></td><td>-2.473***<br>(0.473)</td></tr><tr><td>Med. sch. women x 1950</td><td></td><td></td><td>0.006**<br>(0.002)</td><td></td><td></td><td>0.121*<br>(0.048)</td></tr><tr><td>Birth place, age, marital dummies</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>R2</td><td>0.03</td><td>0.26</td><td>0.26</td><td>0.02</td><td>0.21</td><td>0.21</td></tr><tr><td>N</td><td>919,182</td><td>919,182</td><td>919,182</td><td>919,182</td><td>919,182</td><td>919,182</td></tr></tbody></table>

↑ 1sd wartime employment ⇒ ↑ LFP 0.4pp for white women.
SEA-level impacts by industry

<table><thead><tr><th rowspan="2"></th><th colspan="2">All Women</th><th colspan="2">White Women</th></tr><tr><th>(1)<br>LFP</th><th>(2)<br>Hours Worked</th><th>(3)<br>LFP</th><th>(4)<br>Hours Worked</th></tr></thead><tbody><tr><td>Durable manufacturing</td><td>0.003***<br>(0.001)</td><td>0.103***<br>(0.027)</td><td>0.003***<br>(0.001)</td><td>0.117***<br>(0.030)</td></tr><tr><td>Non-durables</td><td>-0.002<br>(0.001)</td><td>-0.103*<br>(0.043)</td><td>-0.003<br>(0.001)</td><td>-0.118*<br>(0.047)</td></tr><tr><td>Transport, telecom, utilities</td><td>0.000<br>(0.000)</td><td>0.010<br>(0.013)</td><td>0.000<br>(0.000)</td><td>0.007<br>(0.014)</td></tr><tr><td>Wholesale, retail trade</td><td>0.001<br>(0.001)</td><td>0.011<br>(0.037)</td><td>0.001<br>(0.001)</td><td>-0.004<br>(0.038)</td></tr><tr><td>Finance and business</td><td>0.001<br>(0.000)</td><td>0.018<br>(0.014)</td><td>0.001<br>(0.000)</td><td>0.018<br>(0.016)</td></tr><tr><td>Personal services</td><td>-0.002<br>(0.001)</td><td>-0.087*<br>(0.038)</td><td>-0.001<br>(0.001)</td><td>-0.063<br>(0.035)</td></tr><tr><td>Government</td><td>0.001*<br>(0.000)</td><td>0.024<br>(0.015)</td><td>0.001*<br>(0.000)</td><td>0.026<br>(0.016)</td></tr></tbody></table>

* Driven by durable manufacturing, partly offset by decline in non-durables.
<figure><img src="image_52.png" /><figcaption>Pre-trends in effects of WMC employment on LFP</figcaption></figure>
# No effect of wartime mobilization or deaths

## Impact of inductions and war death on FLFP in 1950

<table><thead><tr><th rowspan="2"></th><th colspan="4">LFP</th><th colspan="4">Hours Worked</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td colspan="9"><strong>A. All Women</strong></td></tr><tr><td>Inductions x 1950</td><td>0.002<br>(0.005)</td><td>0.001<br>(0.003)</td><td></td><td></td><td>0.144<br>(0.111)</td><td>0.039<br>(0.116)</td><td></td><td></td></tr><tr><td>War deaths x 1950</td><td></td><td></td><td>0.007*<br>(0.003)</td><td>0.001<br>(0.003)</td><td></td><td></td><td>0.282***<br>(0.081)</td><td>0.119<br>(0.096)</td></tr><tr><td>Birth place, age, marital dummies</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>1940 controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>R2</td><td>0.02</td><td>0.25</td><td>0.03</td><td>0.25</td><td>0.02</td><td>0.20</td><td>0.02</td><td>0.20</td></tr><tr><td>N</td><td>671,921</td><td>671,921</td><td>1,031,743</td><td>1,031,743</td><td>671,921</td><td>671,921</td><td>1,031,743</td><td>1,031,743</td></tr><tr><td colspan="9"><strong>B. White Women</strong></td></tr><tr><td>Inductions x 1950</td><td>0.004<br>(0.006)</td><td>0.002<br>(0.003)</td><td></td><td></td><td>0.250<br>(0.152)</td><td>0.070<br>(0.134)</td><td></td><td></td></tr><tr><td>War deaths x 1950</td><td></td><td></td><td>0.005<br>(0.004)</td><td>0.001<br>(0.004)</td><td></td><td></td><td>0.161<br>(0.123)</td><td>0.151<br>(0.101)</td></tr><tr><td>Birth place, age, marital dummies</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>1940 controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>R2</td><td>0.02</td><td>0.26</td><td>0.03</td><td>0.26</td><td>0.02</td><td>0.21</td><td>0.02</td><td>0.21</td></tr><tr><td>N</td><td>593,076</td><td>593,076</td><td>919,182</td><td>919,182</td><td>593,076</td><td>593,076</td><td>919,182</td><td>919,182</td></tr></tbody></table>
State-level analysis (comparison with literature)

<table><thead><tr><th rowspan="2"></th><th colspan="2">Weeks Worked</th><th colspan="2">Weeks Worked >0</th><th colspan="2">LFP</th><th colspan="2">Hours Worked</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Mobilization x 1950</td><td>14.997***<br>(1.716)</td><td>6.104**<br>(2.137)</td><td>0.107<br>(0.064)</td><td>0.056<br>(0.072)</td><td>-0.034<br>(0.045)</td><td>-0.022<br>(0.056)</td><td>3.556*<br>(1.633)</td><td>10.440***<br>(2.062)</td></tr><tr><td>Share emp. farm x 1950</td><td></td><td>0.223<br>(1.094)</td><td></td><td>0.088**<br>(0.031)</td><td></td><td>0.116***<br>(0.026)</td><td></td><td>5.134***<br>(0.921)</td></tr><tr><td>Share pop white x 1950</td><td></td><td>2.367*<br>(1.147)</td><td></td><td>0.011<br>(0.033)</td><td></td><td>0.034<br>(0.022)</td><td></td><td>2.165**<br>(0.819)</td></tr><tr><td>Med. sch. women x 1950</td><td></td><td>0.406**<br>(0.128)</td><td></td><td>0.020***<br>(0.003)</td><td></td><td>0.013***<br>(0.002)</td><td></td><td>-0.059<br>(0.100)</td></tr><tr><td>Birth place, age, marital dummies</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>R2</td><td>0.01</td><td>0.19</td><td>0.02</td><td>0.19</td><td>0.01</td><td>0.24</td><td>0.01</td><td>0.19</td></tr><tr><td>N</td><td>593,944</td><td>593,944</td><td>593,944</td><td>593,944</td><td>593,944</td><td>593,944</td><td>593,944</td><td>593,944</td></tr></tbody></table>

* Changing definition of weeks worked: 1940 in full-time equivalent, not in 1950. Inflates part-time workers, with mobilization correlated with 1940 part-time.

* Since mobilization unrelated to female wartime employment, difficult to interpret.
* Mixed evidence: what does mobilization capture?

* Using appropriate measurement, little direct effect.

* Indirect implications hold when appropriate measure?
Female labor and WWI in France
* Boehnke and Gay (2022): effect of WWI during interwar period.

* Gay (2023): effect of WWI in post-WWII period.
WWI in France: a shock to the sex ratio

<figure><img src="image_53.png" /></figure>

* 1.3 million French fatalities ⇒ 16% of men aged 15–50 in 1914.
Impact of WWI in France on FLFP?

<figure><img src="image_54.png" /></figure>

WWI Military Death Rates and Changes in FLFP (1901–1911)
# Impact of WWI in France on FLFP?

<figure><img src="image_55.png" /></figure>

WWI Military Death Rates and Changes in FLFP (1911–1921)
* FLFP at département level: censuses 1901–1936.

* FLFP: share of women aged 15+ in labor force.

* Focus on paid employment:

* Farmer's wives to be classified as labor force participant.

* Not consistent in 1901. Maruani and Meron (2012)

* Subtract female farm owners for consistency.
<figure><img src="image_56.png" /></figure>

Average Female Labor Force Participation Rates (%)
# WWI military fatalities: data

* Original dataset:

* Individual military records for all 1.3 million deceased French soldiers.

* Information: département of birth, dates of birth and death.

* Measure of military death rates at the département level:

$$ death\_rate_d = \frac{\text{dead soldiers born in } d}{\underbrace{\text{male pop. aged 15-44 in } d \text{ in 1911}}_{\text{Draft pool in census 1911}}} $$

* Summary statistics:

* Mean = 15%, 25th percentile ≈ 10%, 75th percentile ≈ 20%.

* Minimum = 6%, maximum = 29%.
# WWI military fatalities: distribution

<figure><img src="image_57.png" /></figure>

Military Death Rates Across 87 Départements
* Until end 1914: territorial organization of military recruitment.

* Policies by the Ministry of War to support the industrial war effort:
  ⇒ Up to 15% of soldiers allocated to war industries.

* Pre-war characteristics:

* Correlated with measures of rurality.

* Uncorrelated with other pre-war characteristics.

* Uncorrelated with pre-war levels and trends in FLFP.
# Military fatalities and pre-war characteristics

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Military death rate</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>FLFP</td><td>0.03<br>[0.03]</td><td>-0.06<br>[0.07]</td><td>-0.01<br>[0.07]</td></tr><tr><td>Rural</td><td>0.12***<br>[0.01]</td><td>0.11***<br>[0.03]</td><td>0.16***<br>[0.04]</td></tr><tr><td>Born in dép.</td><td>0.13***<br>[0.03]</td><td>0.15***<br>[0.04]</td><td>0.11*<br>[0.06]</td></tr><tr><td>Other characteristics</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Military region FE</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Départements</td><td>87</td><td>87</td><td>87</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.739</td><td>0.790</td><td>0.838</td></tr></tbody></table>

Other characteristics: fertility, wealth, female education, population density, age, height, share in industry, roads and rails per km2, share cultivated land, direct taxes, banking deposits, distance to war, share religious schools, turnout, distance to war, share literate.
Military fatalities and pre-war trends in FLFP

<figure><img src="image_58.png" /></figure>
Baseline results

$$FLFP_{d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{d,t}$$

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">FLFP</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>Death rate × post</td><td>0.37***<br>[0.08]</td><td>0.35***<br>[0.07]</td></tr><tr><td>Rural</td><td>No</td><td>Yes</td></tr><tr><td>Born in dép.</td><td>No</td><td>Yes</td></tr><tr><td>Départements</td><td>87</td><td>87</td></tr><tr><td>1911 FLFP mean</td><td>31.4</td><td>31.4</td></tr></tbody></table>

* 20% vs 10% military death rates ⇒ FLFP ↑ 4 p.p.

* 4 p.p. ≃ 12% of pre-war FLFP mean (0.4 s.d.).
$$ \text{FLFP}_{d,t} = \sum_{t \neq 1911} \beta_t \text{ death\_rate}_d \times \text{year}_t + \gamma_d + \delta_t + \varepsilon_{d,t} $$

<figure><img src="image_59.png" /></figure>
$$ \text{FLFP}_{d,t} = \sum_{t \neq 1911} \beta_t \text{ death\_rate}_d \times \text{year}_t + \gamma_d + \delta_t + \varepsilon_{d,t} $$

<figure><img src="image_60.png" /></figure>
* Supply channel through post-war marriage market.

* Demand channel through substitution across inputs.

* Persistence of female wartime employment.
* Demographic changes of female marital statuses:

* Increased share of single women.

* Increased share of widowed (and divorced) women.

⇒ Non-married women structurally supply more labor.
* Demographic changes of female marital statuses:

* Increased share of single women.

* Increased share of widowed (and divorced) women.

⇒ Non-married women structurally supply more labor.

* Increased relative labor supply due to negative income shocks:

* War widows (very low subsidies until 1930s).

* Young single women (if deceased soldier in family).

* Married women.

* Lower quality husbands. Abramitky et al. (2011)

* Lower bargaining power in household. Grossbard (2014)
$$Y_{m,a,d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{m,a,d,t}$$

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Share Widowed</th></tr><tr><th>20-29<br>(1)</th><th>30-39<br>(2)</th><th>40-49<br>(3)</th></tr></thead><tbody><tr><td>Age group:</td><td></td><td></td><td></td></tr><tr><td>Death rate × post</td><td>-0.00<br>[0.01]</td><td>0.09***<br>[0.03]</td><td>0.17***<br>[0.03]</td></tr><tr><td>Départements</td><td>87</td><td>87</td><td>87</td></tr><tr><td>1911 outcome mean</td><td>1.3</td><td>4.7</td><td>11.5</td></tr></tbody></table>

Controls: rurality, year and département FE.

* No differential pre-war trends.
$$Y_{m,a,d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{m,a,d,t}$$

<table><thead><tr><th rowspan="2">Dependent variable:<br>Age group:</th><th colspan="3">Share Single</th></tr><tr><th>20-29<br>(1)</th><th>30-39<br>(2)</th><th>40-49<br>(3)</th></tr></thead><tbody><tr><td>Death rate × post</td><td>0.27***<br>[0.07]</td><td>0.23***<br>[0.05]</td><td>0.14***<br>[0.04]</td></tr><tr><td>Départements<br>1911 outcome mean</td><td>87<br>38.9</td><td>87<br>14.6</td><td>87<br>11.2</td></tr></tbody></table>

Controls: rurality, year and département FE.

* No differential pre-war trends.
$$FLFP_{a,d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{a,d,t}$$

<table><thead><tr><th rowspan="3">Dependent variable:<br>Age group:</th><th colspan="3">FLFP</th></tr><tr><th>20-29</th><th>30-39</th><th>40-49</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><th>Death rate × post</th><td>0.27***<br>[0.09]</td><td>0.48***<br>[0.10]</td><td>0.47***<br>[0.10]</td></tr><tr><th>Départements</th><td>87</td><td>87</td><td>87</td></tr><tr><th>1911 outcome mean</th><td>34.7</td><td>30.1</td><td>27.7</td></tr></tbody></table>

Controls: rurality, year and département FE.
$$FLFP_{w,d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{a,d,t}$$

<table><thead><tr><th rowspan="3">Dependent variable:<br>Definition:</th><th colspan="3">FLFP</th></tr><tr><th>Active women /<br>all women</th><th>Active widows /<br>all women</th><th>Active widows /<br>widows</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Death rate × post</td><td>0.26***<br>[0.07]</td><td>0.12***<br>[0.03]</td><td>0.54***<br>[0.11]</td></tr><tr><td>Départements</td><td>87</td><td>87</td><td>87</td></tr><tr><td>1911 outcome mean</td><td>32.9</td><td>5.7</td><td>34.5</td></tr></tbody></table>

Controls: rurality, year and département FE.
* Increase in FLFP compatible with (large) increase in demand for female labor?

* Changes in female wages across occupations with different substitutability relative to male labor.

* Textile manufacturing sector: low substitutability.

* Domestic services sector: high(er) substitutability.
* Nearly exclusively women.

* Occupations: ironers, seamstresses, milliners.

* No other female occupations available before the war.

* City-level hourly wage rates 1901–1926.

* Enables to fix demand curve across départements with ≠ military death rates.
$$ \log w_{o,d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{o,d,t} $$

<table><thead><tr><th rowspan="3">Dependent variable:<br>Occupation:</th><th colspan="3">Log Hourly Wage Rate</th></tr><tr><th>Ironer</th><th>Seamstress</th><th>Milliner</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Death rate × post</td><td>-0.010***<br>[0.004]</td><td>-0.012***<br>[0.003]</td><td>-0.008**<br>[0.003]</td></tr><tr><td>Départements</td><td>87</td><td>87</td><td>87</td></tr><tr><td>1911 outcome mean (francs)</td><td>0.21</td><td>0.23</td><td>0.25</td></tr></tbody></table>

Controls: rurality, year and département FE.

* No differential pre-war trends.
* Balanced gender composition.

* Occupations: cooks and housekeepers.

* City-level yearly wage rates 1913 and 1921.

* Upper bound for changes in demand curve.
$$ \log w_{o,d,t} = \beta \text{ death\_rate}_d \times \text{post}_t + \gamma_d + \delta_t + \varepsilon_{o,d,t} $$

<table><thead><tr><th rowspan="2">Dependent variable:<br>Occupation:</th><th colspan="2">Log Hourly Wage Rate</th></tr><tr><th>Cook<br>(1)</th><th>Housekeeper<br>(2)</th></tr></thead><tbody><tr><td>Death rate × post</td><td>-0.006***<br>[0.002]</td><td>-0.002*<br>[0.001]</td></tr><tr><td>Départements</td><td>87</td><td>87</td></tr><tr><td>1911 outcome mean (francs)</td><td>0.20</td><td>0.16</td></tr></tbody></table>

Controls: rurality, year and département FE.

* No differential pre-war trends.
<figure><img src="image_61.png" /><figcaption>Wartime Employment in Industrial Sector</figcaption></figure>
# Female wartime employment

* Women who entered labor force could have remained, improved skills, information...

* Measure relative intensity of female wartime employment as:

$$ \frac{L_{\text{July 1917}} - L_{\text{July 1914}}}{L_{\text{July 1914}}} $$

* Not correlated with military death rates.

* Same result if use July 1918.
Female wartime employment

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">FLFP</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Death rate × post</td><td>0.35***<br>[0.07]</td><td></td><td>0.35***<br>[0.07]</td></tr><tr><td>Wartime employment × post</td><td></td><td>-0.01<br>[0.01]</td><td>-0.00<br>[0.01]</td></tr><tr><td>Départements</td><td>87</td><td>87</td><td>87</td></tr></tbody></table>

Controls: rurality, year and département FE.
* Limited data to explore mechanisms: need micro-level data
  Gay and Kesztenbaum 2022.

* Long-run consequences and intergenerational transmission.
  Gay (2023)
* Historically unclear how marriage and labor markets interact.

* Relevance of female labor *during* vs *after* wars?

* Other ambiguous consequences: social and political (family norm, social movements, voting)?

* Heavily dependent upon historical context.
Topic 3
Economics history and innovation
* Focus on roots of scientific innovation.
* Focus on roots of scientific innovation.

* Three articles using same historical natural experiment.

* What are the relative roles of human and physical capital in production of scientific knowledge? Waldinger (2016)

* How important are localized peer effects for innovation? Waldinger (2012)

* What are the roles of field-specific knowledge spillovers for innovation? Moser, Voena, and Waldinger (2014)
* Focus on roots of scientific innovation.

* Three articles using same historical natural experiment.

* What are the relative roles of human and physical capital in production of scientific knowledge? Waldinger (2016)

* How important are localized peer effects for innovation? Waldinger (2012)

* What are the roles of field-specific knowledge spillovers for innovation? Moser, Voena, and Waldinger (2014)

* Other studies using same historical natural experiment:

* Effect of faculty quality on PhD student outcomes in mathematics. Waldinger (2010)

* Role of social ties within academic networks in facilitating escape of academics from Nazi Germany. Becker et al. (2021)
* Waldinger (2016), “Bombs, Brains, and Science: The Role of Human and Physical Capital for the Creation of Scientific Knowledge.”

* General research question: what are the inputs of innovation?
* Waldinger (2016), “Bombs, Brains, and Science: The Role of Human and Physical Capital for the Creation of Scientific Knowledge.”

* General research question: what are the inputs of innovation?

* Narrower research question: what are the relative roles of human and physical capital in the production of scientific knowledge?

* Focus on academic scientific knowledge.
* Challenging to estimate:

* Selection of human capital (scientists) into more productive departments.

* Unobserved determinants of physical capital (labs) productivity.

* Complex complementarities between physical and human capital

* Measurement is challenging.
* Challenging to estimate:

* Selection of human capital (scientists) into more productive departments.

* Unobserved determinants of physical capital (labs) productivity.

* Complex complementarities between physical and human capital

* Measurement is challenging.

* Overcome challenges by historical natural experiments:

* Exogenous variation in human capital: dismissal of (mostly Jewish) scientists in Nazi Germany 1933–1940.

* Exogenous variation in physical capital: destruction of science facilities during WWII Allied bombings.
* Dismissal of scientists in Nazi Germany.

* April 1933: National Socialist party passes the "Law for the Restoration of the Professional Civil Service."

* Jewish and "politically unreliable" persons dismissed from civil service positions.

* Anybody with at least one Jewish grandparent dismissed.

* Communist party members dismissed.

* Law extended to Austrian universities in March 1938.
* Dismissal of scientists in Nazi Germany.

* April 1933: National Socialist party passes the "Law for the Restoration of the Professional Civil Service."

* Jewish and "politically unreliable" persons dismissed from civil service positions.

* Anybody with at least one Jewish grandparent dismissed.

* Communist party members dismissed.

* Law extended to Austrian universities in March 1938.

* More than 1,000 academics dismissed from German universities (up to 60% of faculty).

* Most emigrated and got positions at foreign universities (U.S., U.K., Turkey, Switzerland).
* Physicists:

* 15% of dismissed.

* Produced 24% of top journal articles pre-1933.

* Received 64% of citations to articles pre-1933.

* Chemists:

* 14% of dismissed.

* Produced 22% of top journal articles pre-1933.

* Received 23% of citations to articles pre-1933.

* Mathematicians:

* 19% of dismissed.

* Produced 31% of top journal articles pre-1933.

* Received 61% of citations to articles pre-1933.
* Steps in Allied Bombings:

* From 1939: UK RAF targeted raids on German fleet.

* From 1940: RAF bombings on other targets (reservoirs, factories, ...)

* From 1941: RAF bombings on German cities (but small accuracy).

* From 1942: RAF bombings strongly intensified.

* From 1943: US AAF bombings on German targets.

* From 1944: intensification of bombings by US AAF.

* Overall 1.35 million tons of bombs.

* Bombings destroyed 19% of homes in (later) Federal Republic of Germany.

* Scientific departments never targeted, and very inaccurate bombings within cities.
## Allied Bombs Dropped over German Territory

<figure><img src="image_62.png" /></figure>

Source: Waldinger (2016) Figure A2
* Focuses on physicists, chemists, mathematicians in German and Austrian universities.

* Information from registers of all German university professors (1926, 1931, 1940, 1950, 1961, 1970, 1980).

* Complete faculty roster of science departments 1926–1980.

* Select all 35 German or Austrian universities than existed in 1926 (105 science departments).

* Database of 5,716 scientists, 10,387 person-year observations.
<figure><img src="image_63.png" /><figcaption>Number of Scientists in 1926 German Universities</figcaption></figure>

Source: Waldinger (2016) Figure A3
* Scientists' output:

* Publications and citation-weighted publications for each scientist and year.

* Most cited mathematician: Johann von Neumann.

* Departments' output:

* Add individual output within departments.

* Because not yearly data, aggregate within 5-year window.

* Standardize total department output in each subject.
# Dismissals, Bombings, and Other Data

## Dismissals:

* List of Displaced German Scholars (pre-1938 dismissals, no Austria).

* Other sources ⇒ individual data on 90% of all dismissals
# Dismissals, Bombings, and Other Data

* **Dismissals:**

* List of Displaced German Scholars (pre-1938 dismissals, no Austria).

* Other sources ⇒ individual data on 90% of all dismissals

* **Bombings:**

* University archives of funding requests to fund reconstruction.

* Damage of each building in percent (in best case) or verbal description (worse case).
# Dismissals, Bombings, and Other Data

* **Dismissals:**
  * List of Displaced German Scholars (pre-1938 dismissals, no Austria).

* Other sources ⇒ individual data on 90% of all dismissals

* **Bombings:**
  * University archives of funding requests to fund reconstruction.

* Damage of each building in percent (in best case) or verbal description (worse case).

* **Control variables:** university age, share of firms in armament-related industries in 1933, fraction of Jews in city in 1933, distance to iron curtain.
$$ \begin{aligned} \text{Output}_{dt} = & \beta_1 + \sum_{t \neq 1931} \beta_{2t} \text{HCShock}_{1933-40,d} \times \text{Year}_t \\ & + \sum_{t \neq 1940} \beta_{3t} \text{PCShock}_{1942-45,d} \times \text{Year}_t \\ & + \delta_d + \gamma_t + \beta_4 X_{dt} + \varepsilon_{dt} \end{aligned} $$

* Output<sub>dt</sub>: department d research output in year t (publications or citation-weighted publications).

* HCShock<sub>1933-40,d</sub>: dismissal of scientists in department d 1933-40.

* PCShock<sub>1942-45,d</sub>: Allied bombings of department d 1942-45.

* δ<sub>d</sub>: 105 department FE.

* γ<sub>t</sub>: year FE.
## Persistence of 10% Shocks: Publications

<figure><img src="image_64.png" /></figure>

Source: Waldinger (2016), Figure 1. 10% human capital shock is 1.015 scientists. Coefficients from specification subject × year FE, occupation zone × Post 1945, and share city destruction × year FE.
## Persistence of 10% Shocks: Citation-Weighted Publications

<figure><img src="image_65.png" /></figure>

Source: Waldinger (2016), Figure 2. 10% human capital shock is 1.015 scientists. Coefficients from specification subject × year FE, occupation zone × Post 1945, and share city destruction × year FE.
* Subject × year FE.

* City-level destruction × year FE.

* Occupation zone × post 1945 indicator.

* Further controls: Federal state indicator, university age and age squared, number of departments within 50 km, share of firms in armament × year FE, fraction of Jews in city × post 1945, distance to iron curtain.

* Alternative samples: drop Austrian, drop East Germany, Swiss as control.

* Alternative measurements of shocks.

* Little complementarities (triple interaction).
Persistence of 10% Shocks: Publications with All Controls

<figure><img src="image_66.png" /></figure>

Source: Waldinger (2016). Figure 3. 10% human capital shock is 1.015 scientists. Coefficients from specification with all controls
Persistence of High-Quality Dismissals

<figure><img src="image_67.png" /></figure>

Source: Waldinger (2016), Figure 4.
Dismissal and the Quality of Hires

<figure><img src="image_68.png" /></figure>

Source: Waldinger (2016). Figure 5.
* Waldinger (2012), “Peer Effects in Science: Evidence from the Dismissal of Scientists in Nazi Germany.”

* General research question: how important are localized peer effects for innovation?
* Waldinger (2012), “Peer Effects in Science: Evidence from the Dismissal of Scientists in Nazi Germany.”

* General research question: how important are localized peer effects for innovation?

* narrower research question: focus on academic scientific knowledge.
* Challenging to estimate:

* Positive sorting of scientists in same university ⇒ positive correlation of productivity within university not caused by peer effects.

* Unobservables affecting productivity of scientist and their peers.

* Measurement challenges.
* Challenging to estimate:

* Positive sorting of scientists in same university ⇒ positive correlation of productivity within university not caused by peer effects.

* Unobservables affecting productivity of scientist and their peers.

* Measurement challenges.

* Overcome challenges by historical natural experiment:

* Exogenous variation in peer group of researchers in Germany: dismissal of scientists by Nazi government in 1933.
* Dismissal of scientists in Nazi Germany.

* April 1933: National Socialist party passes the “Law for the Restoration of the Professional Civil Service.”

* Jewish and “politically unreliable” persons dismissed from civil service positions.
* Dismissal of scientists in Nazi Germany.

* April 1933: National Socialist party passes the "Law for the Restoration of the Professional Civil Service."

* Jewish and "politically unreliable" persons dismissed from civil service positions.

* More than 1,000 academics dismissed from German universities (15% of all 7,266 university researchers).

* Most emigrated right away and got positions at foreign universities (U.S., U.K., Turkey, Switzerland).

* Most dismissals in 1933–1934.
# Dismissal of Scientists

## Quality of Dismissed Scholars

<table><thead><tr><th rowspan="3"></th><th colspan="4">Physics</th><th colspan="4">Chemistry</th><th colspan="4">Mathematics</th></tr><tr><th colspan="4">Dismissed 1933–1934</th><th colspan="4">Dismissed 1933–1934</th><th colspan="4">Dismissed 1933–1934</th></tr><tr><th>All</th><th>Stayers</th><th>No.</th><th>% Loss</th><th>All</th><th>Stayers</th><th>No.</th><th>% Loss</th><th>All</th><th>Stayers</th><th>No.</th><th>% Loss</th></tr></thead><tbody><tr><td>Researchers (beginning of 1933)</td><td>287</td><td>248</td><td>39</td><td>13.6</td><td>466</td><td>405</td><td>61</td><td>13.1</td><td>224</td><td>183</td><td>41</td><td>18.3</td></tr><tr><td>Researchers (beginning of 1933)</td><td>287</td><td>248</td><td>39</td><td>13.6</td><td>466</td><td>405</td><td>61</td><td>13.1</td><td>224</td><td>183</td><td>41</td><td>18.3</td></tr><tr><td>No. of chaired professors</td><td>109</td><td>97</td><td>12</td><td>11.0</td><td>156</td><td>136</td><td>20</td><td>12.8</td><td>117</td><td>99</td><td>18</td><td>15.4</td></tr><tr><td>Average age (1933)</td><td>49.5</td><td>50.2</td><td>45.1</td><td>—</td><td>50.4</td><td>50.5</td><td>49.7</td><td>—</td><td>48.7</td><td>50</td><td>43</td><td>—</td></tr><tr><td>No. of Nobel Laureates</td><td>15</td><td>9</td><td>6</td><td>40.0</td><td>14</td><td>11</td><td>3</td><td>21.4</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Publications 1925–1932</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average publications</td><td>0.47</td><td>0.43</td><td>0.71</td><td>20.5</td><td>1.69</td><td>1.59</td><td>2.31</td><td>17.9</td><td>0.33</td><td>0.27</td><td>0.56</td><td>31.1</td></tr><tr><td>Average citations (citation weighted)</td><td>5.10</td><td>3.53</td><td>14.79</td><td>39.4</td><td>17.25</td><td>16.07</td><td>25.05</td><td>19.0</td><td>1.45</td><td>0.93</td><td>3.71</td><td>46.8</td></tr><tr><td>% co-authored</td><td>32.0</td><td>32.1</td><td>31.4</td><td>—</td><td>75.2</td><td>74.8</td><td>76.9</td><td>—</td><td>16.9</td><td>15.1</td><td>20.3</td><td>—</td></tr><tr><td>% co-authored with faculty (with dismissed)</td><td>11.1</td><td>10.3</td><td>14.5</td><td>—</td><td>11.8</td><td>12.3</td><td>9.7</td><td>—</td><td>9.9</td><td>9.7</td><td>10.2</td><td>—</td></tr><tr><td>% co-authored with faculty (same unit) (with dismissed)</td><td>(3.1)</td><td>(2.0)</td><td>(8.1)</td><td>—</td><td>(1.9)</td><td>(1.9)</td><td>(2.0)</td><td>—</td><td>(4.6)</td><td>(3.8)</td><td>(6.1)</td><td>—</td></tr><tr><td>Publications 1935–1938</td><td>3.7</td><td>2.9</td><td>7.4</td><td>—</td><td>4.3</td><td>4.4</td><td>4.1</td><td>—</td><td>2.6</td><td>1.8</td><td>4.3</td><td>—</td></tr><tr><td>Average publications</td><td>(1.5)</td><td>(0.5)</td><td>(5.9)</td><td>—</td><td>(0.9)</td><td>(0.9)</td><td>(1.1)</td><td>—</td><td>(1.7)</td><td>(1.2)</td><td>(2.7)</td><td>—</td></tr><tr><td>Average publications (citation weighted)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% co-authored</td><td>34.0</td><td>0.35</td><td>0.32</td><td>—</td><td>1.24</td><td>0.55</td><td>—</td><td>—</td><td>0.20</td><td>0.15</td><td>—</td><td>—</td></tr><tr><td>% co-authored with faculty (with dismissed)</td><td>6.9</td><td>7.0</td><td>—</td><td>—</td><td>3.9</td><td>2.0</td><td>—</td><td>—</td><td>6.0</td><td>4.0</td><td>—</td><td>—</td></tr><tr><td>% co-authored with faculty (same unit) (with dismissed)</td><td>(0.6)</td><td>(4.0)</td><td>—</td><td>—</td><td>(0.4)</td><td>(2.0)</td><td>—</td><td>—</td><td>(0.0)</td><td>(4.0)</td><td>—</td><td>—</td></tr></tbody></table>

Source: Waldinger (2012), Table 3.
* Focuses on scientists (physicists, chemists, mathematicians) at German universities.

* Information from registers of all German university scientists (annual 1925–1938).

* Select rank of at least lecturer.

* Information on specialization of all scientists.
* Collect publications of each researcher in academic journals.

* For each researcher, two yearly productivity measures:

* Sum of publications in top journals in a given year.

* Citation-weighted publications: sum of citations to all articles published in a given year in any journal included in Web of Science in first 50 years after publication.
* Different types of peer effects:

* Joint research projects involving joint publication.

* Discussions and collaborations without co-publishing.

* Peer pressure affecting productivity.

* Peer funding and networks affecting productivity.

* Empirical approach differentiates two dimensions of peer effects:

* Quality of peer effects measured by mean average productivity of peers (researchers of same specialty in same department).

* Number of peer interactions measured by number of peers at department.

* Use one-year lags to account for publication lags.
$$ \#\text{Publications}_{iut} = \beta_1 + \beta_2 (\text{Peer Quality})_{ut} + \beta_3 (\#\text{of Peers})_{ut} \\ + \beta_4 \text{Age } \text{FE}_{iut} + \beta_5 \text{Year } \text{FE}_{t} + \beta_6 \text{Ind. } \text{FE}_{i} + \varepsilon_{iut} $$

* Scientist *i* at university *u* in year *t*.

* Instrument peer group size and quality by dismissals.
## Effects of Dismissals on Department Size and Peer Quality (Physics)

<figure><img src="image_69.png" /></figure>

Source: Waldinger (2012), Figure 1.
* Two first-stage regressions.

* One to instrument peer quality, with "Dismissal-Induced Fall in Peer quality": difference average peer quality before 1933 – average peer quality before 1933 among stayers.

* First first-stage regression:

$$ \text{Peer Quality}_{ut} = \gamma_1 + \gamma_2 (\text{Dismissal-Induced } \downarrow \text{ in Peer Quality})_{ut} \\ + \gamma_3 (\#\text{Dismissed})_{ut} \\ + \gamma_4 \text{Age } \text{FE}_{iut} + \gamma_5 \text{Year } \text{FE}_t + \gamma_6 \text{Ind. } \text{FE}_i + \varepsilon_{iut} $$
* Second first-stage regression:

$$ \begin{aligned} \#\text{of Peers}_{ut} = & \delta_1 + \delta_2 (\text{Dismissal-Induced} \downarrow \text{in Peer Quality})_{ut} \\ & + \delta_3 (\#\text{Dismissed})_{ut} \\ & + \delta_4 \text{Age FE}_{iut} + \delta_5 \text{Year FE}_t + \delta_6 \text{Ind. FE}_i + \varepsilon_{iut} \end{aligned} $$
* Reduced form:

$$ 
\begin{aligned}
\#\text{Publications}_{iut} = & \theta_1 + \theta_2 (\text{Dismissal-Induced } \downarrow \text{ in Peer Quality})_{ut} \\
& + \theta_3 (\#\text{Dismissed})_{ut} \\
& + \theta_4 \text{Age } \text{FE}_{iut} + \theta_5 \text{Year } \text{FE}_t + \theta_6 \text{Ind. } \text{FE}_i + \varepsilon_{iut}
\end{aligned}
 $$

* Compares changes in publications from the pre- to the post-withdrawal period for researchers in affected departments to the change between the two periods for unaffected researchers.
<figure><img src="image_70.png" /><figcaption>Effect of Dismissal on Stayers</figcaption></figure>

Source: Waldinger (2012). Figure 2.
## Reduced Form

<table><thead><tr><th rowspan="3">Dependent variable</th><th colspan="2">Physics</th><th colspan="2">Chemistry</th><th colspan="2">Mathematics</th></tr><tr><th colspan="2">Cit. weighted</th><th colspan="2">Cit. weighted</th><th colspan="2">Cit. weighted</th></tr><tr><th>Publications</th><th>publications</th><th>Publications</th><th>publications</th><th>Publications</th><th>publications</th></tr></thead><tbody><tr><td>Dismissal induced fall<br>in peer quality</td><td>0.029<br>(0.015)</td><td>0.312<br>(0.235)</td><td>0.012<br>(0.015)</td><td>0.383<br>(0.303)</td><td>0.022<br>(0.031)</td><td>-0.464<br>(0.337)</td></tr><tr><td>Number dismissed</td><td>-0.021<br>(0.017)</td><td>-0.017<br>(0.302)</td><td>-0.018<br>(0.009)*</td><td>-0.130<br>(0.222)</td><td>-0.018<br>(0.015)</td><td>-0.016<br>(0.167)</td></tr><tr><td>Age dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual FE</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>2261</td><td>2261</td><td>3584</td><td>3584</td><td>1538</td><td>1538</td></tr><tr><td>No. of researchers</td><td>258</td><td>258</td><td>413</td><td>413</td><td>183</td><td>183</td></tr><tr><td>R-squared</td><td>0.39</td><td>0.25</td><td>0.67</td><td>0.54</td><td>0.32</td><td>0.20</td></tr></tbody></table>

Source: Waldinger (2012), Table 4.
## First Stages

<table><thead><tr><th rowspan="2">Dependent variable</th><th colspan="2">Physics</th><th colspan="2">Chemistry</th><th colspan="2">Mathematics</th></tr><tr><th>Peer quality</th><th>Department size</th><th>Peer quality</th><th>Department size</th><th>Peer quality</th><th>Department size</th></tr></thead><tbody><tr><td>Dismissal induced fall in peer quality</td><td>-0.644**<br>(0.099)</td><td>-0.147<br>(0.130)</td><td>-1.114**<br>(0.196)</td><td>0.011<br>(0.110)</td><td>-1.355**<br>(0.149)</td><td>-0.228<br>(0.174)</td></tr><tr><td>Number dismissed</td><td>0.017<br>(0.098)</td><td>-0.570**<br>(0.117)</td><td>-0.047<br>(0.162)</td><td>-0.998**<br>(0.091)</td><td>0.160**<br>(0.053)</td><td>-0.470**<br>(0.062)</td></tr><tr><td>Age dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual FE</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>2261</td><td>2261</td><td>3584</td><td>3584</td><td>1538</td><td>1538</td></tr><tr><td>No. of researchers</td><td>258</td><td>258</td><td>413</td><td>413</td><td>183</td><td>183</td></tr><tr><td>R<sup>2</sup></td><td>0.59</td><td>0.90</td><td>0.66</td><td>0.91</td><td>0.70</td><td>0.81</td></tr><tr><td>F—Test on instruments</td><td>81.9</td><td>103.10</td><td>18.3</td><td>64.3</td><td>47.8</td><td>66.2</td></tr><tr><td>Cragg—Donald EV statistic</td><td colspan="2">12.8</td><td colspan="2">89.8</td><td colspan="2">46.7</td></tr></tbody></table>

Source: Waldinger (2012), Table 5.
## First Stages

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th colspan="2">OLS</th><th colspan="2">IV</th></tr><tr><th></th><th colspan="4">Physics</th></tr><tr><th>Dependent variable:</th><th>Publications</th><th>Publications</th><th>Cit. weigt. Pubs.</th><th>Cit. weigt. Pubs.</th></tr></thead><tbody><tr><th>Peer quality</th><td>0.004<br>(0.005)</td><td>-0.054<br>(0.035)</td><td>-0.048<br>(0.075)</td><td>-0.488<br>(0.496)</td></tr><tr><th>Department size</th><td>-0.007<br>(0.004)</td><td>0.035<br>(0.034)</td><td>-0.177**<br>(0.062)</td><td>0.016<br>(0.553)</td></tr><tr><th>Age dummies</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Year dummies</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Individual FE</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th>Observations</th><td>2261</td><td>2261</td><td>2261</td><td>2261</td></tr><tr><th>No. of researchers</th><td>258</td><td>258</td><td>258</td><td>258</td></tr><tr><th>R<sup>2</sup></th><td>0.39</td><td></td><td>0.25</td><td></td></tr><tr><th>Cragg-Donald EV Stat.</th><td></td><td>12.79</td><td></td><td>12.79</td></tr></tbody></table>

Source: Waldinger (2012), Table 6.
* Drop 1933 and 1934.

* Independent from age of dismissal scientists.

* Independent from baseline size or quality of department.

* Robust to including university FE and university-specific time trends.

* Independent from dismissal at sub-field level or top scientists.
* No evidence of localized peer effects in science.

* Potential explanations:

* No long-run data.

* Strong integration of pre-WWII German scientific community.

* Science very specialized.

* Mature fields at the time.
* Moser, Voena, and Waldinger (2014) "German Jewish Emigrés and US Invention."

* Strong migration of German Jewish to the US fleeing from Nazi Germany:

* 133,000 German Jewish émigrés by 1944.

* 2,400 academics among them.

* Did they generate spillovers to US innovation?

* Focus on chemists, which innovation is well measured by patents.
Emigré Chemistry Professors

* Collect information on all 535 chemistry professors and postdoctoral fellows at German and Austrian Universities in 1931–1933.

* 93 chemistry professors were dismissed between 1933 and 1941.

* Among these, 26 emigrated to the US and remained professionally active.
* Collect information on all US patents issued to all 535 German chemistry professors between 1920 and 1970:

* Description of invention and USPTO class.

* Date of patent.

* Location of patentee.

* Employment history of patentee to ensure match.
# US Patents of German Chemists

* Collect information on all US patents issued to all 535 German chemistry professors between 1920 and 1970:

* Description of invention and USPTO class.

* Date of patent.

* Location of patentee.

* Employment history of patentee to ensure match.

* Total patents 1920–1970 by 535 German chemists:

* 946 patents.

* 282 patents by 43 dismissed German chemists.

* 157 patents by 13 German Jewish émigrés to the US.
<figure><img src="image_71.png" /><figcaption>US Patents per Year by German Chemists</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 1.
* Collect all US patents in 166 classes with patents by German chemists 1920–1970.

* Keep only patents by inventors born in the U.S.

* Also collect information on 964k US inventors of 1.3M patents in research fields of German chemists 1920–1970.
<figure><img src="image_72.png" /><figcaption>## US Patents per Class and Year by Domestic US Inventors</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 2A.
<figure><img src="image_73.png" /><figcaption>## US Patents per Class and Year by Domestic US Inventors</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 2B.
$$ \text{Patents by US inventors}_{c,t} = \alpha_0 + \beta \text{émigré class}_c \cdot \text{post}_t + \gamma' \mathbf{X}_{c,t} + \delta_t + f_c + \varepsilon_{c,t} $$

* Technology class c and year t between 1920 and 1970.

* émigré class_c: indicator for technology class c includes at least one patent by German Jewish émigré (number of émigré patents in alternative specifications).

* post_t: indicator for post 1933 in Germany and post 1938 in Austria.

* Control group: classes with patents by other German chemists but not émigrés.

* X: controls for variation in patenting at level of research field and year.
OLS Regressions

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Émigré class × post</td><td>105.222***<br>(22.203)</td><td>91.712***<br>(19.212)</td><td>84.803***<br>(18.950)</td><td>75.439***<br>(19.326)</td><td></td><td></td><td></td><td></td></tr><tr><td>Number émigré patents<br>× post</td><td></td><td></td><td></td><td></td><td>5.848*<br>(3.058)</td><td>4.992*<br>(2.561)</td><td>4.527**<br>(2.182)</td><td>3.991**<br>(1.956)</td></tr><tr><td>Number foreign patents</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Quadratic class age</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Patent pools</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Class fixed effects</td><td>Yes</td><td>Yes</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>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td></tr><tr><td>R<sup>2</sup></td><td>0.783</td><td>0.845</td><td>0.849</td><td>0.851</td><td>0.779</td><td>0.842</td><td>0.846</td><td>0.848</td></tr></tbody></table>

Source: Moser, Voena, and Waldinger (2014), Table 2. Given mean 241 patents per class and year in classes with patents by other German chemists, arrival of émigré chemist increased US patenting by 31%.
<figure><img src="image_74.png" /><figcaption>Year-Specific OLS Estimates</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 3.
# Effect of Emigrés on Domestic Invention

* Sources of endogeneity of OLS estimates:

* Dismissed inventors may enter fields becoming more productive.

* Historical evidence: negative selection of Jewish dismissed inventors, and work in less productive fields.
* Sources of endogeneity of OLS estimates:

* Dismissed inventors may enter fields becoming more productive.

* Historical evidence: negative selection of Jewish dismissed inventors, and work in less productive fields.

* Use pre-1933 patents of dismissed chemists to instrument for 1920–1970 patents of émigrés to the US.

* Exploits fact that research decisions by German Jewish chemists prior dismissal independent from expectations of research in U.S. after 1933.

* First stage:

$$ \text{Emigré class}_c \cdot \text{post}_t = \zeta_0 + \phi \cdot \text{pre-1933 dismissed class}_c \cdot \text{post}_t + \theta' \mathbf{X}_{c,t} + \lambda_t + \mu_c + \nu_{c,t} $$
First-Stage and Reduced Form

<table><thead><tr><th></th><th colspan="6">First stage</th><th colspan="3">Reduced form</th></tr><tr><th></th><th colspan="2">Emigré class<br>× post</th><th colspan="2">Number émigré patents<br>× post</th><th rowspan="2">(5)</th><th rowspan="2">(6)</th><th rowspan="2">(7)</th><th rowspan="2">(8)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Dismissed class<br>× post</td><td>0.370***<br>(0.081)</td><td>0.339***<br>(0.079)</td><td></td><td></td><td>80.821***<br>(23.155)</td><td>57.752***<br>(19.436)</td><td></td><td></td></tr><tr><td>Number dismissed patents<br>× post</td><td></td><td></td><td>1.384***<br>(0.442)</td><td>1.303***<br>(0.435)</td><td></td><td></td><td>35.595***<br>(6.547)</td><td>22.330***<br>(6.339)</td></tr><tr><td>Number foreign patents</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Quadratic class age</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Patent pools</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Class fixed effects</td><td>Yes</td><td>Yes</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>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td></tr><tr><td>R<sup>2</sup></td><td>0.801</td><td>0.809</td><td>0.770</td><td>0.773</td><td>0.779</td><td>0.849</td><td>0.782</td><td>0.849</td></tr><tr><td>F-statistic</td><td>20.80</td><td>18.25</td><td>9.79</td><td>8.99</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: Moser, Voena, and Waldinger (2014), Table 3.
<figure><img src="image_75.png" /><figcaption>Patents by Domestic Inventors</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 4.
Instrumental Variable Regression

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Émigré class × post</td><td>218.707***<br>(60.614)</td><td>170.136***<br>(57.992)</td><td></td><td></td></tr><tr><td>Number émigré patents × post</td><td></td><td></td><td>25.717***<br>(8.750)</td><td>17.137**<br>(6.909)</td></tr><tr><td>Number foreign patents</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Quadratic class age</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Patent pools</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Class fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td></tr></tbody></table>

Source: Moser, Voena, and Waldinger (2014), Table 4. Given mean 241 patents per class and year in classes with patents by other German chemists, arrival of émigré chemist increased US patenting by 71%. LATE estimates increase in patenting by US inventors for classes in which émigrés to the US patented because dismissed chemists had patented in same class before 1933.
* Examine changes in patenting for 210k US inventors who had patented before 1933 in a research field of German chemists.

* Estimate differential effects of émigrés on incumbent inventors depending on share of incumbent's patents in research fields of émigrés.
## Patenting by Incumbent Innovators in Fields of Emigrés vs Other German Chemists

<figure><img src="image_76.png" /></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 5.
$$ \text{Patenting}_{i,t} = \alpha + \beta \text{share of patents in émigré classes}_i \cdot \text{post}_t + \gamma' \mathbf{Z}_{i,t} + \delta_t + f_i + \varepsilon_{i,t} $$

* Patenting$_{i,t}$: indicator for incumbent US inventor i patented at least one invention in year t.

* Z$_{i,t}$: controls for variation in productivity over life cycle of inventor.

* Incumbent inventor FE and year FE.

* Instrument share of inventor's overall patents in research fields of émigrés with share of inventor's pre-1933 patents in fields with pre-1933 patents of dismissed chemists.
## First Stage and Reduced Form

<table><thead><tr><th></th><th colspan="2">First stage</th><th colspan="2">Reduced form</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Share of pre-1933 patents<br>in dismissed classes × post</td><td>0.403***<br>(0.086)</td><td>0.402***<br>(0.086)</td><td>-0.0003<br>(0.001)</td><td>-0.009***<br>(0.002)</td></tr><tr><td>Quadratic time to first patent</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Quadratic time since first patent</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Inventor fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>10,730,910</td><td>10,730,910</td><td>10,730,910</td><td>10,730,910</td></tr><tr><td>F-statistic</td><td>21.83</td><td>21.65</td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.434</td><td>0.434</td><td>0.045</td><td>0.147</td></tr></tbody></table>

Source: Moser, Voena, and Waldinger (2014), Table 7.
OLS and IV Estimates

<table><thead><tr><th></th><th colspan="2">OLS (linear probability)</th><th colspan="2">IV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Share of patents in émigré classes × post</td><td>0.002**<br>(0.001)</td><td>-0.007***<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>-0.022***<br>(0.006)</td></tr><tr><td>Quadratic time to first patent</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Quadratic time since first patent</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Inventor fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>10,730,910</td><td>10,730,910</td><td>10,730,910</td><td>10,730,910</td></tr><tr><td>R<sup>2</sup></td><td>0.045</td><td>0.147</td><td>—</td><td>—</td></tr></tbody></table>

Source: Moser, Voena, and Waldinger (2014), Table 6. Incumbent inventors who had a 10% larger share of their patents in émigré classes became 22% less likely to patent an invention after 1932.

⇒ Knowledge spillovers from émigrés to incumbent inventors unlikely to be driving force behind increase in US patenting after 1933.
* Arrival of émigrés might have encouraged US scientists to switch into fields of émigrés, or attract a new group of US scientists.

* Measure researcher's year of entry in new field by first patent in émigré class.

* Distinguish new inventors from entry by former inventors in other fields.
# Mechanisms: Entry into Research Fields of Emigrés

<figure><img src="image_77.png" /><figcaption>## Entry of US Patentees into Research Fields of Emigrés</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 7.
$$ \text{Entry}_{c,t} = \alpha + \beta \text{émigré class}_c \cdot \text{post}_t + \gamma' \mathbf{X}_{c,t} + \delta_t + f_c + \varepsilon_{c,t} $$

* Entry$_{c,t}$: new researchers per class and year (measured by researcher first patent in class c).

* émigré class$_c$: indicator if class c includes at least one patent by an émigré.

* Class FE and year FE.

* Instrument fields of émigrés by pre-1933 research fields of dismissed chemists.
# Mechanisms: Entry into Research Fields of Emigrés

<figure><img src="image_78.png" /><figcaption>## Entry of US Patentees into Research Fields</figcaption></figure>

Source: Moser, Voena, and Waldinger (2014), Figure 8.
## OLS and IV Estimates

<table><thead><tr><th rowspan="3"></th><th colspan="4">OLS</th><th colspan="4">Instrumental Variables</th></tr><tr><th colspan="2">Entrants into field</th><th colspan="2">Entrants into patenting</th><th colspan="2">Entrants into field</th><th colspan="2">Entrants into patenting</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Émigré class × post</td><td>73.799***<br>(15.674)</td><td>58.181***<br>(14.715)</td><td>53.434***<br>(12.522)</td><td>43.967***<br>(12.261)</td><td>162.287***<br>(44.195)</td><td>142.119***<br>(45.982)</td><td>116.707***<br>(34.565)</td><td>109.466***<br>(37.863)</td></tr><tr><td>Number foreign patents</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Quadratic class age</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Patent pools</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Class fixed effects</td><td>Yes</td><td>Yes</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>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td><td>8,466</td></tr><tr><td>R<sup>2</sup></td><td>0.781</td><td>0.835</td><td>0.763</td><td>0.805</td><td>0.767</td><td>0.824</td><td>0.750</td><td>0.792</td></tr></tbody></table>

Source: Moser, Voena, and Waldinger (2014), Table 9. Given mean 166 entrants per class and year in classes with patents by other German chemists, arrival of émigré chemist increased entrants 35%.
* Evidence that collaborators of émigrés switched into research fields of émigrés after 1933.

* Amplification through network of collaborations including co-inventors of these induced co-inventors of Jewish émigrés.
* Evidence that collaborators of émigrés switched into research fields of émigrés after 1933.

* Amplification through network of collaborations including co-inventors of these induced co-inventors of Jewish émigrés.

* Further including junior émigrés not professor at time of dismissal suggests broader effects.
Topic 4
Economics history and urban economics
# Why turn to history in urban economics?

Hanlon and Heblich (2020)

* Existence of cities are the result of slow-moving processes.

* Urban infrastructures characterized by path dependence.

* Expand set of experiences of rare events to inform policy.

* Historical natural experiments for identification.

* Exogenous variation.

* Fewer interdependencies.

* Economic development characterized by long processes.
* Innovations in historical research.

* Origins of cities development.

* Spatial distribution of economic activity within cities.

* Cities and growth
Innovations in historical urban economics
Publications in historical urban economics

<figure><img src="image_79.png" /></figure>

Source: Hanlon and Heblich (2020), Figure 1a
* Increased digitization of archival data sources with spatial dimension:

* Censuses

* Birth and death records

* Patent records

* Immigration lists

* Tax records

* See Giuliano and Matranga (2020)
* Increased digitization of archival data sources with spatial dimension:

* Censuses

* Birth and death records

* Patent records

* Immigration lists

* Tax records

* See Giuliano and Matranga (2020)

* Archival material digitization technologies:

* Optical character recognition (OCR) technologies (though low quality images, heterogeneous typesetting and fonts, hand-written documents).

* Crowdsourcing to citizen historians (e.g., zooniverse platform). See special issue of *Historical Methods* (2019), 52(3)

* Machine learning for digitizing maps. Combes et al. (2020)
* Standardized frame of reference (HGIS). Gay (2021)

* Matching spatial units.

* Non-overlapping levels of aggregation: modifiable area unit problem (MAUP).

* Boundary changes over time. Eckert et al. (2020)
* Standardized frame of reference (HGIS). Gay (2021)

* Matching spatial units.

* Non-overlapping levels of aggregation: modifiable area unit problem (MAUP).

* Boundary changes over time. Eckert et al. (2020)

* Data source with in-text spatial information (e.g., patents):
computational linguistics. Perlmann (2015)
* Standardized frame of reference (HGIS). Gay (2021)

* Matching spatial units.

* Non-overlapping levels of aggregation: modifiable area unit problem (MAUP).

* Boundary changes over time. Eckert et al. (2020)

* Data source with in-text spatial information (e.g., patents): computational linguistics. Perlmann (2015)

* Linking individuals over time. Abramitzky et al. (2020) Bailey et al. (2020)
* Increasingly available, rich information (boundaries, buildings, neighborhood, land use, transportation infrastructures...).

* Key for structures of cities and neighborhood.
  Hornbeck and Keniston (2017)

* Challenges in digital and automatic processing.
  Combes et al. (2020)

* Often lack precision, yet key for geographical RDDs.
  Sequeira et al. (2020)
* To compensate for lack of historical information, use time invariant factors that explain urban patterns:

* Geological conditions.

* Climate conditions.

* Terrain and soil characteristics

* Interact characteristics with time FE to assess when time invariant factor gained importances. Nunn and Qian (2011)
* To compensate for lack of historical information, use time invariant factors that explain urban patterns:

* Geological conditions.

* Climate conditions.

* Terrain and soil characteristics

* Interact characteristics with time FE to assess when time invariant factor gained importances. Nunn and Qian (2011)

* Genetic markers and linguistic traits. Spolaore and Wacziarg (2009)
* Use models from outside economics to predict historical conditions.

* Atmospheric models (e.g., wind patterns for sailing time). Pascali (2017)

* Engineering rules for in-land transportation cost and time. Bogart et al (2019)

* Environment models for pollution dissemination. Heblich et al (2016)

* See *Annual Review* article “Quantitative Spatial Economics.” Redding and Rossi-Hansberg (2017)
* Eckert et al (2020) "A Method to Construct Geographical Crosswalks with an Application to US Counties since 1790."

* Historical data at heterogeneous levels of aggregation.

* Mapping heterogeneous geographies requires cross-walks within and across years.

⇒ Develop geographical crosswalks Python routines to build consistent panels.
Boundary changes over time

* Eckert et al (2020) "A Method to Construct Geographical Crosswalks with an Application to US Counties since 1790."

* Historical data at heterogeneous levels of aggregation.

* Mapping heterogeneous geographies requires cross-walks within and across years.

⇒ Develop geographical crosswalks Python routines to build consistent panels.

* Map US county-level data from 1790–2010 to:

* 1990 and 2010 county boundaries.

* 1990 commuting zones.

* Generic code can be applied to construct crosswalks for arbitrary geographies.
# Method to construct geographical crosswalks

* General idea: intersecting maps of the same territory partitioned in different ways.

* Two shapefiles required (in same projection):

* Reporting map for spatial units for which data is reported.

* Reference map for spatial units to which data is to be re-aggregated.

* Intersect maps to generate a new map of smaller mutually exclusive spatial subunits.

* Recombine to form partition in either reporting or reference map, with areas of subunits as weights.
New York state county boundaries

<figure><img src="image_80.png" /><figcaption>(a) 1790 COUNTIES</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>(b) 2010 COUNTIES</figcaption></figure>

Source: Eckert et al (2020), Figure 1
Intersection between New York state county boundaries

<figure><img src="image_82.png" /></figure>
* Compute for each subunit (disjoint) their share of total land area.

* For each region in reporting map, compute share of its subunits in total land area.

* Each spatial unit in the two maps has weights associated.

* Map 1790 population to 2010 counties:

* Take population counts by county in 1790.

* Multiply 1790 county population counts by associated weights.

* This uniformly distribute population across subunits it contains.

* Sum these counts across subunits contained within each of 2010 county delineations.
* Underlying assumption: variable distributed proportionally to land area size across subunits within each spatial unit of reporting map.

* For plausibility: use most disaggregated maps for which data are reported to construct geographical crosswalks.

* Use for stock variables.

* Different weights required for average outcomes (e.g., average wages).

* To use Python code, only specify reporting and reference map.
* Apply method to construct crosswalk for US counties since 1790.

* Three features responsible to vast changes in counties:

* Territorial expansion from 1790 (Louisiana, Oregon, Mexico, Alaska) to 1898 (Hawaii).

* The creation of the Union and integration US territories until 1959.

* Changing county boundaries due to population changes (generally partitions).
Territorial history of the United States

<figure><img src="image_83.png" /></figure>

Source: Eckert et al (2020), Figure 3
<figure><img src="image_84.png" /><figcaption>The territory and the Union States over time</figcaption></figure>

Source: Eckert et al (2020), Figure 4
Changing county numbers and boundaries

<table><thead><tr><th rowspan="2">Year</th><th rowspan="2">Total Subdivisions</th><th rowspan="2">2010 Territory</th><th colspan="4">Fraction of Crosswalk Weights between</th></tr><tr><th>0.4 and 0.6</th><th>0.2 and 0.8</th><th>0.01 and 1</th></tr></thead><tbody><tr><td>1790</td><td>292</td><td>0.2221</td><td>0.018</td><td>0.065</td><td>0.265</td></tr><tr><td>1800</td><td>427</td><td>0.232</td><td>0.021</td><td>0.085</td><td>0.291</td></tr><tr><td>1810</td><td>587</td><td>0.4882</td><td>0.020</td><td>0.071</td><td>0.278</td></tr><tr><td>1820</td><td>779</td><td>0.4797</td><td>0.021</td><td>0.075</td><td>0.306</td></tr><tr><td>1830</td><td>1002</td><td>0.4955</td><td>0.022</td><td>0.084</td><td>0.259</td></tr><tr><td>1840</td><td>1285</td><td>0.4945</td><td>0.021</td><td>0.078</td><td>0.210</td></tr><tr><td>1850</td><td>1632</td><td>0.8305</td><td>0.015</td><td>0.059</td><td>0.195</td></tr><tr><td>1860</td><td>2126</td><td>0.8377</td><td>0.012</td><td>0.042</td><td>0.157</td></tr><tr><td>1870</td><td>2336</td><td>0.8377</td><td>0.010</td><td>0.036</td><td>0.129</td></tr><tr><td>1880</td><td>2614</td><td>0.9982</td><td>0.010</td><td>0.028</td><td>0.085</td></tr><tr><td>1890</td><td>2799</td><td>0.9982</td><td>0.007</td><td>0.020</td><td>0.062</td></tr><tr><td>1900</td><td>2848</td><td>1</td><td>0.006</td><td>0.018</td><td>0.050</td></tr><tr><td>1910</td><td>2963</td><td>1</td><td>0.003</td><td>0.012</td><td>0.035</td></tr><tr><td>1920</td><td>3075</td><td>1</td><td>0.001</td><td>0.003</td><td>0.017</td></tr><tr><td>1930</td><td>3110</td><td>1</td><td>0</td><td>0.001</td><td>0.009</td></tr><tr><td>1940</td><td>3108</td><td>1</td><td>0</td><td>0.001</td><td>0.008</td></tr><tr><td>1950</td><td>3111</td><td>1</td><td>0</td><td>0.001</td><td>0.007</td></tr><tr><td>1960</td><td>3134</td><td>1</td><td>0</td><td>0.001</td><td>0.007</td></tr><tr><td>1970</td><td>3141</td><td>1</td><td>0</td><td>0.001</td><td>0.005</td></tr><tr><td>1980</td><td>3137</td><td>1</td><td>0</td><td>0.001</td><td>0.003</td></tr><tr><td>1990</td><td>3141</td><td>1</td><td>0</td><td>0</td><td>0.002</td></tr><tr><td>2000</td><td>3141</td><td>1</td><td>0</td><td>0</td><td>0.002</td></tr><tr><td>2010</td><td>3143</td><td>1</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
* Historical shapefiles (including territories outside US) from NHGIS.
Manson et al (2017)

* Application:

* For each decade 1790-2000: intersect county shapefile with 2010 county shapefile.

* For each county-decade, compute share of its area and assign to 2010 list of counties.

* Repeat for 1990 counties and 1990 commuting zones reference maps.
Application: US counties since 1790

County-level population in 1850 for different county delineations

<figure><img src="image_85.png" /><figcaption>(a) 1850 COUNTIES, 1850 US TERRITORY</figcaption></figure>

<figure><img src="image_86.png" /><figcaption>(b) 2010 COUNTIES, 1850 US TERRITORY</figcaption></figure>

<figure><img src="image_87.png" /><figcaption>(c) 1850 COUNTIES, 1850 UNION TERRITORY</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>(d) 2010 COUNTIES, 1850 UNION TERRITORY</figcaption></figure>

Source: Eckert et al (2020), Figure 5
* Need to assume uniform distribution of stock variable across subunits within units.

* Are these units similarly meaningful across time, especially if use unit FE in panel setting?

* Might generate biases if externalities at county level not absorbed by the FE.

* Think about meaning of “units” and changes over time, especially in relation to process of interest.
* Use administrative information on changes without resorting to map.

* Example: Third-Republic Information System Gay (2021)

* Need “building blocks” (e.g., communes) and attribute them to aggregated units.

* Challenge: infra-building-block constituencies.
Origins of cities development
* Central issues in urban economics:

* How to explain the distribution of population across space?

* How to explain the existence and development of cities?

* Part of broader importance of history for long-run development.
  Nunn (2014)
* Central issues in urban economics:

* How to explain the distribution of population across space?

* How to explain the existence and development of cities?

* Part of broader importance of history for long-run development.
  Nunn (2014)

* Main answers:

* Location fundamentals.

* Productivity effects of agglomeration.

* Amenities and disamenities.

* Trade and market access.

* Education and human capital.
* Role of location fundamentals for city distributions and growth?

* Broad use of historical natural experiments.

* Bombings during wars, e.g. US bombings of Japan during WWII. Davis and Weinstein (2002)
    ⇒ No persistent effects.

* Economic shocks to cities e.g., shock to cotton imports in Britain during US Civil War. Hanlon (2017)
    ⇒ Persistent impact on city size.

* Temporary relocation of populations e.g., resettlement of expelled Germans after WWII. Schumann (2014)
    ⇒ Persistent impact on city size.

⇒ Location fundamentals importance ↑ as differences across locations ↑.
* Importance of location fundamentals is time-dependent on advantages provided by introduction of new technologies:

* Soil quality and potato introduction in Europe. Nunn and Qian (2011)

* Coal reserves and the Industrial Revolution in Europe. Fernihough and O'Rourke (2014)

* Ports naturally accommodating large ships and containerized shipping. Brooks et al (2016)

* Interactions location fundamentals and technological change is active area of research.
# Bombs and break points

* Davis and Weinstein (2002) "Bones, Bombs, and Break Points: The Geography of Economic Activity."
* Davis and Weinstein (2002) "Bones, Bombs, and Break Points: The Geography of Economic Activity."

* Two theories of distribution of economic activity across space, and predictions to temporary shock:

* Agglomeration effects:

* Importance of knowledge spillovers, labor-market pooling, proximity of supply and demand when trade is costly.

* Strong role of path dependence.

* Temporary shocks have permanent consequences.

* Location fundamentals:

* Location-specific characteristics might be randomly allocated, but systematically determine later growth.

* Importance varies depending on substitutes and interactions with technology.

* Temporary shocks have temporary consequences.
* Test theories through historical natural experiment of Allied bombing of Japanese cities during WWII.

* Data:

* 303 Japanese cities with population > 30,000 in 1925.

* Population counts 5-year intervals 1925–1965 (except 1945→1947).

* Population growth rates 1925–1940, 1940–1947, 1947–1965.
# Bombs and break points

* Test theories through historical natural experiment of Allied bombing of Japanese cities during WWII.

* Data:

* 303 Japanese cities with population > 30,000 in 1925.

* Population counts 5-year intervals 1925–1965 (except 1945→1947).

* Population growth rates 1925–1940, 1940–1947, 1947–1965.

* Measure of intensity of shocks:

* Number of dead and missing city residents due to war.

* Number of building destroyed per resident.

* Control for (limited) US and Japanese government expenditures for reconstruction.
* Magnitude of the shocks

* Allied strategic bombing of 66 Japanese cities.

* Destruction of 1/2 structures (2.2 million buildings), 2/3 productive capacity.

* 300,000 Japanese killed.

* Nuclear bombings:

* Hiroshima: 2/3 city destroyed, 20% of population (80,000).

* Nagasaki: 40% city destroyed, 8.5% of population (25,000).
* Variance of the shocks

* 80% of cities (37% of urban population) untouched by bombings (e.g., Kyoto).

* Variability in destruction within cities.

* Other factors: evolving defense of Japanese cities, topography, growth of B-29s in service.
* Size of any city at any point in time:

$$s_{it} = \Omega_{i} + \varepsilon_{it}$$

* $s_{it}$: log city i share of total population at time t.

* $\Omega_{i}$: initial city size.

* $\varepsilon_{it}$: city-specific shock.
* Size of any city at any point in time:

$$s_{it} = \Omega_i + \varepsilon_{it}$$

* $s_{it}$: log city i share of total population at time t.

* $\Omega_i$: initial city size.

* $\varepsilon_{it}$: city-specific shock.

* Persistence of shock to population:

$$\varepsilon_{it+1} = \rho\varepsilon_{it} + \nu_{it+1}$$

* $\rho \in [0, 1]$

* $\nu_{it+1}$ is iid error term.
* Evolution of city size:

$$s_{it+1} - s_{it} = (\rho - 1)\nu_{it} + [\nu_{it+1} + \rho(1 - \rho)\varepsilon_{it-1}] \quad (1)$$
* Evolution of city size:

$$s_{it+1} - s_{it} = (\rho - 1)\nu_{it} + [\nu_{it+1} + \rho(1 - \rho)\varepsilon_{it-1}] \quad (1)$$

* Empirical strategy:

* Estimate (1) by regressing growth rate 1947–1960 on growth rate 1940–1947 (get $\rho - 1$).

* Instrument growth rate 1940–1947 with buildings destroyed and deaths per capita.
* Evolution of city size:

$$s_{it+1} - s_{it} = (\rho - 1)\nu_{it} + [\nu_{it+1} + \rho(1 - \rho)\varepsilon_{it-1}] \quad (1)$$

* Empirical strategy:

* Estimate (1) by regressing growth rate 1947–1960 on growth rate 1940–1947 (get $\rho - 1$).

* Instrument growth rate 1940–1947 with buildings destroyed and deaths per capita.

* Two theories:

* $\rho - 1 = 0$: all shocks are permanent, city share is random walk with $s_{it+1} = s_{it} = \varepsilon_{it+1}$.

* $\rho \in [0, 1)$: city share is stationary, shocks dissipate.

* $\rho - 1 = -1$: shock was dissipated by 1960
# Bombs and break points

Effects of bombing on cities > 30,000 inhabitants

<figure><img src="image_89.png" /></figure>

Source: Davis and Weinstein (2002), Figure 1. Only contains cities with positive casualty rates
<table><caption>Instrumental variables equation</caption><thead><tr><th>Independent variable</th><th>Coefficient</th></tr></thead><tbody><tr><td>Constant</td><td>0.213<br>(0.006)</td></tr><tr><td>Deaths per capita</td><td>-0.665<br>(0.506)</td></tr><tr><td>Buildings destroyed per capita</td><td>-2.335<br>(0.184)</td></tr><tr><td>R<sup>2</sup>:</td><td>0.409</td></tr><tr><td>Number of observations:</td><td>303</td></tr></tbody></table>

Source: Davis and Weinstein (2002), Table 2. Dependent variable: rate of growth in city population 1940–1947.
2SLS estimates of impact of bombing on cities

<table><thead><tr><th rowspan="2">Independent variable</th><th colspan="2">Dependent variable =<br>growth rate<br>of population<br>between<br>1947 and<br>1960</th><th>Dependent<br>variable =<br>growth rate<br>of population<br>between<br>1947 and<br>1965</th></tr><tr><th>(i)</th><th>(ii)</th><th>(iii)</th></tr></thead><tbody><tr><td>Growth rate of population<br>between 1940 and 1947</td><td>-1.048<br>(0.097)</td><td>-0.759<br>(0.094)</td><td>-1.027<br>(0.163)</td></tr><tr><td>Government reconstruction<br>expenses</td><td>1.024<br>(0.387)</td><td>0.628<br>(0.298)</td><td>0.392<br>(0.514)</td></tr><tr><td>Growth rate of population<br>between 1925 and 1940</td><td></td><td>0.444<br>(0.054)</td><td>0.617<br>(0.092)</td></tr><tr><td>R<sup>2</sup>:</td><td>0.279</td><td>0.566</td><td>0.386</td></tr><tr><td>Number of observations:</td><td>303</td><td>303</td><td>303</td></tr></tbody></table>

Source: Davis and Weinstein (2002), Table 3. Instruments: deaths per capita and buildings destroyed per capita.
* Objection to interpretation: refugees.

* Population movement driven by people forced to live elsewhere, but later came back.

* Argument cannot be made for Hiroshima and Nagasaki.

⇒ Full recovery within 20 years.
<figure><img src="image_90.png" /><figcaption>Population growth</figcaption></figure>

Source: Davis and Weinstein (2002), Figure 2
# Productivity effects of agglomeration

* City size and clustering of specific industries generate productivity advantages (suppliers proximity, localized knowledge, workers pool).

* Localized knowledge and supply of capable entrants in tire manufacturing in Akron, Ohio.
  Buenstorf and Klepper (2009, 2010)

* Inter-industry local externalities (carriage and wagon industry) in regional agglomeration of US auto industry.
  Cabral et al (2018)

* Origins of industry clusters among automakers in Detroit and semi-conductor manufacturers in Silicon Valley.
  Klepper (2010)

* Impact of proximity on productivity of European classical composers in late 18th–early 19th century.
  Borowiecki (2013)

⇒ Cross-industry vs within-industry spillovers question.
* Implications for place-based policies.

* Tennessee Valley Authority project 1930s–1960s ⇒ long-run impact on manufacturing but not agriculture thanks to agglomeration economies. Kline and Moretti (2013)

* Subsidies to West German regions to compensate for loss in market access ⇒ persistent impact on economic density due to physical infrastructures investment. Ehrlich and Seidel (2018)

* Transfer of capital of West Germany to Bonn in 1949 and public employment. Becker et al. (2021)
* Becker, Heblich, and Sturm (2021) “The Impact of Public Employment: Evidence From Bonn.”

* General research question: spillover effects of changes in spatial distribution of public employment on the private sector.
* Becker, Heblich, and Sturm (2021) "The Impact of Public Employment: Evidence From Bonn."

* General research question: spillover effects of changes in spatial distribution of public employment on the private sector.

* Use creation of West German government in Bonn as a source of variation to assess causal impact of public employment on spatial distribution of private sector activity.
* Mass $L$ of workers, mobile across cities, endowed with unit labor supplied inelastically with zero disutility.
* Mass $L$ of workers, mobile across cities, endowed with unit labor supplied inelastically with zero disutility.

* Consumption in city $c$ over:

* Tradeable goods: $C_c^T$

* Non-tradeable goods: $C_c^N$

* Housing: $C_c^H$

* Amenities: $B_c$

* Cobb-Douglas utility function:

$$U_c = B_c (C_c^T)^\mu (C_c^N)^\theta (C_c^H)^{1-\mu-\theta}$$
* Transportation cost $\tau_{ic} > 1$ for tradables produced in $i$ consumed in $c$.

* Marketable price index CES (Dixit-Stiglitz):

$$P_c^T = \left[ \sum_i n_i^T (p_i^T \tau_{ic})^{1-\sigma} \right]^{1/(1-\sigma)}$$

* Tradables $n_i^T$ produced in $i$ face same elasticity of demand and charge same equilibrium price to city $c$: $p_{ic}^T = p_i^T \tau_{ic}$.
* Transportation cost $\tau_{ic} > 1$ for tradables produced in $i$ consumed in $c$.

* Tradeable price index CES (Dixit-Stiglitz):

$$P_c^T = \left[ \sum_i n_i^T (p_i^T \tau_{ic})^{1-\sigma} \right]^{1/(1-\sigma)}$$

* Tradables $n_i^T$ produced in $i$ face same elasticity of demand and charge same equilibrium price to city $c$: $p_{ic}^T = p_i^T \tau_{ic}$.

* Non-tradables can only be consumed in city where produced:

$$P_c^N = \left[ n_c^N (p_c^N)^{1-\sigma} \right]^{1/(1-\sigma)} = p_c^N (n_c^N)^{1/(1-\sigma)}$$
* Both tradables and non-tradables produced by monopolistically competitive firms with same production technology.

* Fixed labor cost $F$ and constant variable cost.

* Labor $l$ to produce $x$ units with productivity of firms in city $i$ $\varphi_i$:

$$l = F + \frac{x}{\varphi_i}$$

* Equilibrium supply function (constant mark-up over marginal cost):

$$p_i = \left( \frac{\sigma}{\sigma - 1} \right) \frac{w_i}{\varphi_i}$$
* Housing supply function:

$$P_c^H = (L_c)^\gamma$$

* $\gamma$: elasticity of house price wrt employment.
* Public sector produces global public good that affects all agents equally in all cities.

* Utility derived from public good normalized to zero.

* Number of public sector workers is constant.

* Policy decision: distribution of public employment across cities $L_i^G$.

* Public sector workers paid $w_i^G = w_i$, the private sector rate in city i.

* Public sector financed through flat income tax t levied on all workers:

$$t \sum_{i} (w_{i} L_{i}) = \sum_{i} (w_{i} L_{i}^{G})$$
* Public sector spillover to productivity of private sector worker in city i:

$$ \varphi_i = \xi_i (L_i^G)^\alpha $$

* $\xi_i$: sources of productivity other than public employment.

* $\alpha$: elasticity of productivity of private sector to size of public sector.
* Public sector spillover to productivity of private sector worker in city i:

$$ \varphi_i = \xi_i (L_i^G)^\alpha $$

* $\xi_i$: sources of productivity other than public employment.

* $\alpha$: elasticity of productivity of private sector to size of public sector.

* Mechanisms of spillovers:

* Classical knowledge spillovers (e.g., about public policy).

* Boosts in local demand from public sector, equivalent to productivity shock.
* Public sector spillover to consumption amenities in city i:

$$B_c = \psi_i (L_c^G)^\delta$$

* $\psi_i$: sources of amenities other than public employment.

* $\delta$: elasticity of amenities to size of public sector.
* Public sector spillover to consumption amenities in city i:

$$B_c = \psi_i (L_c^G)^\delta$$

* $\psi_i$: sources of amenities other than public employment.

* $\delta$: elasticity of amenities to size of public sector.

* Mechanisms of spillovers:

* Public sector workers could improve local schools and other public goods.

* Ability to divert larger share of national public spending to these locations.
* Workers are freely mobile.

* Equal utility across cities implies:

$$ \frac{w_c B_c}{(P_c^T)^\mu (P_c^N)^\theta (P_c^H)^{1-\mu-\theta}} = k, \quad \forall c $$

* Higher wages and amenities make a city more attractive.

* Higher prices make a city less attractive.
* Simulate impact of changes in location of public employment on distribution of private sector employment in a two-city version of the model.

* Parameter values:

* Elasticity of substitution $\sigma = 5$.

* Share of expenditure on housing $1 - \mu - \theta = 1/3$.

* Share of expenditure on tradable and non-tradable $\mu = \theta = 1/3$.

* Share of public employment in total employment $L_G/L = 12\%$.

* Trade cost between two cities $\tau_{ic} = 1.25$.

* Elasticity of house prices wrt employment or population $\gamma = 0.57$.

* Compare different spillover values when shifting public employment from city 2 to city 1 (ex-ante symmetric):

* No spillovers.

* Intermediate spillovers: $\alpha = \delta = 0.004$.

* Large spillovers: $\alpha = \delta = 0.008$.
## Simulating the impact of public employment

<figure><img src="image_91.png" /></figure>

Source: Becker et al (2021), Figure 1
* Short run (before workers relocation):

* Demand from public sector met by reduction in employment in private sector.

* Housing prices are unchanged (population unchanged).

* Because constant share spent on non-tradables, change in employment from tradable sector (imports from other cities).

* Price of tradables ↑ ⇒ ↓ real wages and utility.

* Thanks to productivity spillovers, firms can pay higher wages.

* Thanks to amenities spillovers, utility ↑.
* Short run (before workers relocation):

* Demand from public sector met by reduction in employment in private sector.

* Housing prices are unchanged (population unchanged).

* Because constant share spent on non-tradables, change in employment from tradable sector (imports from other cities).

* Price of tradables ↑ ⇒ ↓ real wages and utility.

* Thanks to productivity spillovers, firms can pay higher wages.

* Thanks to amenities spillovers, utility ↑.

* Long run (mobility, if spillovers large enough):

* Inflow of workers from city 2 to city 1.

* Production in private sector ↑.

* Inflow until house prices high enough to equalize utility.
* After WWII, Germany divided in East Germany, West Germany, parts to Poland and Soviet Union.

* West Germany funded in 1949, and capital voted as Bonn (other contenders: Frankfurt, Stuttgart, Kassel).

* Fall of Berlin Wall in November 1989.

* Formal reunification on October 1990.

* Relocation of capital to Berlin on June 1991.
* Census data before and after WWII: 1925–1987.

* 40 control cities in West Germany: 20 just below/above Bonn's population in 1939 (Bonn 31st largest West German city in 1939).

* State-level statistics for city-level employment data in 10 sectors.

* Public employment includes public and social security administration, but excludes health, education, and state-owned enterprises.

* Data on consumption amenities, gross value added per worker, wages, housing prices.
Bonn and the control cities

<figure><img src="image_92.png" /></figure>

Source: Becker et al (2021), Figure 2
* Difference-in-differences comparing Bonn to 40 control cities:

$$Y_{ct} = \gamma + T_c + T_c \times D_t + \lambda_t + \varepsilon_{ct}$$

* $Y_{ct}$: employment in city c at time t.

* $T_c$: indicator for Bonn.

* $D_t$: indicator for post-1949.

* $\lambda_t$: time FE.
* Difference-in-differences comparing Bonn to 40 control cities:

$$Y_{ct} = \gamma + T_c + T_c \times D_t + \lambda_t + \varepsilon_{ct}$$

* $Y_{ct}$: employment in city c at time t.

* $T_c$: indicator for Bonn.

* $D_t$: indicator for post-1949.

* $\lambda_t$: time FE.

* Issues when one control unit:

* Similar control group.

* Clustering at city level underestimates standard errors.

⇒ Synthetic control city for Bonn (on pre-1949 levels).

Abadie and Gardeazable (2003) Abadie et al (2010)
Public sector employment

<figure><img src="image_93.png" /><figcaption>Public sector employment (Synthetic control)</figcaption></figure>

<figure><img src="image_94.png" /></figure>

Source: Becker et al (2021), Figure 3. Synthetic: 1987 vs pre = +15,600 workers.
Private sector employment

<figure><img src="image_95.png" /></figure>

Source: Becker et al (2021), Figure 3. Synthetic: 1987 vs pre = +13,400 workers.
⇒ Multiplier of public to private employment = 0.86.
## Total employment

<figure><img src="image_96.png" /></figure>

Source: Becker et al (2021), Figure 3. Synthetic: 1987 vs pre = +29,000 workers.
Tradable vs non-tradable private employment (synthetic control)

<figure><img src="image_97.png" /></figure>

<figure><img src="image_98.png" /></figure>

Source: Becker et al (2021), Figure 5. ↑ 1 public = +1.05 non-tradable, -0.19 tradable.
* Evidence for amenity spillovers in Bonn, proxied by cultural expenditures, theater budgets and visitors, number of artists, doctors, hospital beds.

* No evidence for productivity spillovers in Bonn, proxied by gross value added.

* Evidence for small increase in nominal wages in Bonn.

* Evidence for higher housing prices in Bonn.
* Evidence for amenity spillovers in Bonn, proxied by cultural expenditures, theater budgets and visitors, number of artists, doctors, hospital beds.

* No evidence for productivity spillovers in Bonn, proxied by gross value added.

* Evidence for small increase in nominal wages in Bonn.

* Evidence for higher housing prices in Bonn.

* Using moments from empirical analysis, estimate best fits for spillover parameters: productivity spillovers close to zero, amenities spillovers positive and very large.
* Role of amenities and disamenities for city growth:

* Location of baroque opera houses across Germany attracted high human capital workers with positive effects on city growth.
    Falck et al (2012)

* Industrial pollution in late 19th century Britain reduced city growth through lower productivity of workers and firms. Hanlon (2020)
* Impact of trade or “market access” on the development of local economies.

* Local impact of transportation infrastructure improvement on spatial distribution of economic activity in colonial India. Donladson (2018)

* Impact of railroads in 19th century US on local development and input factors reallocation.
  Donaldson and Hornbeck (2016) Hornbeck and Rotemberg (2019)

* Long-run effects of Roman roads network across Europe for inter-regional trade. Flückiger et al (2019) Dalgaard et al (2018)
# Education and human capital

* Educational institutions:

* Impact of medieval universities using founding of new universities after Papal Schism of 1386. Cantoni and Yuchtman (2014)

* Founding of US universities on local innovation rates. Andrews (2020)

* Transfers of knowledge or skills across urban areas:

* Access to the printing press in 15th century. Dittmar (2011)

* Huguenot (skilled labor) Diaspora to Prussia after expulsion from France in 17th century. Hornung (2014)

* Education and industrialization:

* Education and industrialization in 19th century Prussia. Becker et al (2011)

* Knowledge elite (purchasers of Encyclopédie in mid-18th century France) and industrialization. Squicciarini and Voigtländer (2015)
Spatial distribution of economic activity within cities
* Key questions with historical perspective:

* Equilibrium distribution of residents, workers, and land rents.

* Urban infrastructures (roads, sewers) and shape and size of cities.

* Source of contemporaneous residential segregation.
* Key questions with historical perspective:

* Equilibrium distribution of residents, workers, and land rents.

* Urban infrastructures (roads, sewers) and shape and size of cities.

* Source of contemporaneous residential segregation.

* Topics:

* Transport infrastructure.

* Urban health amenities.

* Residential amenities.

* Segregation.

* Zoning and urban policies.
* Before industrial revolution, city structures constrained by distance (walking). Voth (2001).

* The first transportation revolution: railways and subways. Bogart (2014)

* Constraint on city structure relaxed.

* Employment and residence became increasingly segregated. Heblich et al (2020)

* Led to highly localized production and residential externalities (e.g., Berlin using variation from division and reunification 1930s–2000s). Ahlfeldt et al (2015)
* The second transportation revolution: cars and trucks.
  Glaeser and Kohlase (2004)

* First wave: trucks freed firms' location choices ⇒ sub-centers.

* Second wave: urban highways freed residential choices ⇒
suburbanization (e.g., 1947 highway plan in U.S. as source of
variation). Baum-Snow (2007)
* The second transportation revolution: cars and trucks.
  Glaeser and Kohlsh (2004)

* First wave: trucks freed firms' location choices ⇒ sub-centers.

* Second wave: urban highways freed residential choices ⇒
    suburbanization (e.g., 1947 highway plan in U.S. as source of
    variation). Baum-Snow (2007)

* Strong persistence of urban layout (e.g., coordinating effects of rectangular grids in cities, Manhattan since 1811). O'Grady (2014)
  ⇒ Rapidly expanding area of research!
* Baum-Snow (2007) “Did Highways Cause Suburbanization?”

* General research question: see the title.

* Paradox: 1950–1990, populations in central US cities ↓ 17% while population in metropolitan areas ↑ 72%.

* Contribution of new limited access highways?

* Answer: 1/3 of relative changes in population.
# The Interstate Highway System

* Federal Aid Highway Act of 1933: development of national system of interstate highways.

* In 1947, 37k miles of roadways approved by federal government.

* Funding mostly provided by 1956 Highway Revenue Acts.

* Database of yearly highway development 1950–1990.

* Primary measure: segments of reads connecting central business district of central city to region outside central city.

* 1947 plan not correlated with changes in commuting demand, but trade.

* Segments in the 1947 plan are strong predictors of constructed segments 1950–1990 ⇌ used planned segments as instrument.
# The Interstate Highway System

The projected system of interstate highways in 1947

<figure><img src="image_99.png" /></figure>

Source: Baum-Snow (2007), figure 1
* Land use theory: higher commuting speed implies lower population density.

* New highway segment ⇒ ↑ supply of land for given commuting time in section of city.

* Prices of rent ↓ throughout metro area.

* Implications, when space is normal good:

* Population in metro area spread out along new highways.

* Central city population decreases with number of highways.
Development patterns in Austin, TX

<figure><img src="image_100.png" /></figure>

Source: Baum-Snow (2007), figure 2
* Long difference estimates (1950–1990):

$$ \Delta \log N_i^c = \delta_0 + \delta_1 \Delta \text{ray}_i + \delta_2 r_{ci} + \delta_3 \Delta \tilde{w}_i + \delta_4 \Delta \log N_i^{MSA} + \delta_5 \Delta \tilde{G}_i + \varepsilon_i $$

* $N_i^c$: central city population MSA i.

* $\text{ray}_i$: highway segment from central city to outside region.

* $r_c$: central city radius.

* $N_i^{MSA}$: MSA population.

* $\tilde{w}_i$: mean log annual personal income.

* $\tilde{G}_i$: GINI of income distribution of MSA.

* Panel estimates

* Panel based on census years 1950 to 1990

* Better account for timing of adjustments and speed of convergence.

* But lower quality data for in-between years.
## Long-difference IV regressions

<table><caption>Large MSAs in 1950</caption><thead><tr><th rowspan="2"></th><th rowspan="2">OLS3</th><th colspan="5">Change in log population in constant geography<br>central cities</th></tr><tr><th>IV1</th><th>IV2</th><th>IV3</th><th>IV4</th><th>IV5</th></tr></thead><tbody><tr><td>Change in number of rays</td><td>-.059<br>(.014)**</td><td>-.030<br>(.022)</td><td>-.106<br>(.032)**</td><td>-.123<br>(.029)**</td><td>-.114<br>(.026)**</td><td>-.101<br>(.046)**</td></tr><tr><td>1950 central city radius</td><td>.080<br>(.014)**</td><td></td><td>.111<br>(.023)**</td><td>.113<br>(.023)**</td><td>.106<br>(.023)**</td><td>.125<br>(.021)**</td></tr><tr><td>Change in simulated log income</td><td>.084<br>(.378)</td><td></td><td></td><td>.048<br>(.417)</td><td>-6.247<br>(6.174)</td><td>-.137<br>(.480)</td></tr><tr><td>Change in log of MSA population</td><td>.363<br>(.082)**</td><td></td><td></td><td>.424<br>(.094)**</td><td>.374<br>(.079)**</td><td>.405<br>(.108)**</td></tr><tr><td>Change in Gini coeff of simulated income</td><td></td><td></td><td></td><td></td><td>-23.416<br>(23.266)</td><td></td></tr><tr><td>Log 1950 MSA population</td><td></td><td></td><td></td><td></td><td></td><td>-.062<br>(.062)</td></tr><tr><td>Constant</td><td>-.640<br>(.260)*</td><td>-.203<br>(.078)*</td><td>-.359<br>(.076)**</td><td>-.588<br>(.281)*</td><td>.4580<br>(5.091)</td><td>-.611<br>(.265)*</td></tr><tr><td>Observations</td><td>139</td><td>139</td><td>139</td><td>139</td><td>139</td><td>139</td></tr><tr><td>R-squared</td><td>.39</td><td>.00</td><td>.01</td><td>.30</td><td>.33</td><td>.37</td></tr></tbody></table>

Source: Baum-Snow (2017), table 4
Panel IV regressions

<table><thead><tr><th></th><th colspan="6">Large MSAs in 1950</th></tr><tr><th></th><th colspan="6">Log central city population</th></tr><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Number of rays<br>(1990 Rays) ×<br>(Fraction of Ray<br>miles completed at t)</td><td>-0.111<br>(0.016)**</td><td>-0.142<br>(0.026)**</td><td>-0.140<br>(0.028)**</td><td></td><td></td><td></td></tr><tr><td>Log simulated income</td><td></td><td>-0.083<br>(0.117)</td><td>-0.061<br>(0.109)</td><td>-0.097<br>(0.016)**</td><td>-0.089<br>(0.012)**</td><td>-0.086<br>(0.013)**</td></tr><tr><td>Log MSA population</td><td></td><td>0.266<br>(0.104)*</td><td>0.263<br>(0.105)*</td><td></td><td>-0.288<br>(0.075)**</td><td>-0.229<br>(0.077)**</td></tr><tr><td>Simulated Gini<br>coefficient</td><td></td><td></td><td>-0.623<br>(1.106)</td><td></td><td>0.294<br>(0.100)**</td><td>-1.415<br>(0.847)</td></tr><tr><td>MSA Fixed Effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R-Squared</td><td>0.20</td><td>0.22</td><td>0.22</td><td>0.14</td><td>0.56</td><td>0.57</td></tr></tbody></table>

Source: Baum-Snow (2017), table 6
* Water infrastructures.

* Equally important factor for internal organization of cities (and health distribution): pluming and sewer systems.

* Before sewerage infrastructure, endemic cholera epidemics, aggravated by industrialization and increased density.
  Ferrie and Troesken (2008) Kesztembaum and Rosenthal (2011)

* Review of water infrastructure and urban mortality in the U.S.
  Beach (2021)
* Water infrastructures.

* Equally important factor for internal organization of cities (and health distribution): pluming and sewer systems.

* Before sewerage infrastructure, endemic cholera epidemics, aggravated by industrialization and increased density.
  Ferrie and Troesken (2008) Kesztembaum and Rosenthal (2011)

* Review of water infrastructure and urban mortality in the U.S.
  Beach (2021)

* Air pollution.

* Industrialization increased revenues but at dramatic health cost (e.g., coal smoke and population growth in Britain 1851–1911).
  Hanlon (2020)

* Interactions between urban pollution and spread of epidemics (e.g., Spanish Influenza in U.S. cities).
  Clay and Troesken (2011)
<figure><img src="image_101.png" /><figcaption>Epidemics in New York City</figcaption></figure>
* Factors influencing amenity value of neighborhoods:

* Public water systems (plumbing and sewers).

* Pollution.

* Public safety (e.g., professionalization of police force in early 19th century-London). Bindler and Hjalmarsson (2019)

* Natural amenities (lakes, mountains). Lee and Lin (2018)

* Urban infrastructures (roads, public transportation, opera houses, architecture...). Ahlfeldt and Holman (2018)

* Amenities have strong persistence influences on urban segregation and distribution of income within cities.
* Very large literature on origins and lasting effects of racial segregation within cities, especially in the U.S.

* Neighborhood-level segregation doubled 1880–1940. Logan and Parman (2017)

* Segregation accelerated 1940–1970 with white flight from city centers to suburbs and black migration from rural South to industrial North. Boustan (2010) Shertzer and Walsh (2019)

* Important implications for neighborhood-level provision of public goods.
* Very large literature on origins and lasting effects of racial segregation within cities, especially in the U.S.

* Neighborhood-level segregation doubled 1880–1940. Logan and Parman (2017)

* Segregation accelerated 1940–1970 with white flight from city centers to suburbs and black migration from rural South to industrial North. Boustan (2010) Shertzer and Walsh (2019)

* Important implications for neighborhood-level provision of public goods.

* Role of transportation infrastructures:

* Development of transportation facilitated / perpetuated segregation (e.g., highways and suburbanization).

* Highways generated little benefits to (black) housing owners in city centers, but decreased land value (lower rents, increased pollution and noise). Brinkman and Lin (2020)
* Most prominent urban policy (in U.S.): zoning. Fischel (2004)

* First wave early 20th century: protect home owners from industrial development when urban mobility improved.

* Persistent implications for city structures (e.g., Chicago zoning ordinance of 1923). Shertzer et al. (2018)
* Most prominent urban policy (in U.S.): zoning. Fischel (2004)

* First wave early 20th century: protect home owners from industrial development when urban mobility improved.

* Persistent implications for city structures (e.g., Chicago zoning ordinance of 1923). Shertzer et al. (2018)

* Relationship between land development and urban policies after fires: Boston 1873 and San Francisco 1906.
Hornbeck and Keniston (2017) Siodla (2015, 2017)

* Other restrictions: vertical development in U.S. cities (Chicago, New York) and London after WWII bombings.
Barr (2013) Ahlfeldt and McMillen (2018) Koster and Dericks (2020)
* Hornbeck and Keniston (2017) "Creative Destruction: Barriers to Urban Growth and the Great Boston Fire of 1872."

* Research question: is urban growth restricted when landowners do not internalize positive spillover effects?

* Use Boston Fire of 1872 as a source of widespread simultaneous reconstruction, encouraging upgrades in nearby buildings.

* Fire destroyed 776 buildings in downtown Boston in 22 hours (11% of total real estate value of Boston).

* Reconstruction managed privately (non zoning law or city coordination).

* Fire in commercial neighborhood: upgrades in economic spaces.
# The Great Boston Fire of 1872

Historical downtown Boston and the burned area

<figure><img src="image_102.png" /></figure>
# The Great Boston Fire of 1872

## Plot-level records:

* Land and building value and occupant's industry and business value at plot level for 1867-1894.

* Plots in burned and surrounding areas.

* 44k land plots pooled over 7 years.
* Plot-level records:

* Land and building value and occupant's industry and business value at plot level for 1867-1894.

* Plots in burned and surrounding areas.

* 44k land plots pooled over 7 years.

* Pre-fire values in burned and unburned areas:

* Burned area was most valuable parts of downtown Boston.

* Burned areas more valuable prior to the Fire.

* But no difference in pre-Fire values trends across burned and unburned areas.

* "Close" vs "distant" sample with 400 meters cutoff.
* Theoretical framework to assess cross-building externalities:

* Each landowner owns one (homogeneous) plot.

* Maximizes net present value of rents from plot $r(q, Q, \omega)$

* $q$: building quality.

* $Q$: average quality of nearby buildings.

* $\omega$: city overall productivity.

* Spillovers 1: quality of nearby buildings $\partial r/\partial Q > 0$.

* Spillovers 2: marginal returns to building quality wrt nearby quality $\partial^2 r/\partial q\partial Q > 0$.

* Direct spillover and multiplier effects in burned and unburned areas.
* Cost of replacing building of quality $q'$: $c(q')$.

* Buildings destroyed with probability $d$.

* Total value is Bellman equation:

$$V(q, Q, \omega) = \max \begin{cases} r(q, Q, \omega) + \delta \mathbb{E} [V(q, Q', \omega')] \\ r(q^*, Q, \omega) + \delta \mathbb{E} [V(q^*, Q', \omega')] - c(q^* \end{cases}$$

* where $q^*$ maximizes:

$$r(q, Q, \omega) + \delta [(1 - d)V(q, Q', \omega') + dV(0, Q, \omega')] - c(q^*)$$

* $q^*$: optimal choice of building quality when choosing to replace old building or forced to reconstruct when destroyed.
* Absent of destruction, landowners replace building when obsolete (q) and city productivity has increased enough.

* Total value of owning building is composed of:

* Land value component: $V(0, Q, \omega) = V(q, Q, \omega)$

* Building value component: $V(q, Q, \omega) - V(q, Q, \omega)$.

* Building rents have Cobb-Douglas form: $r(q, Q, \omega) = q^{\alpha} (Q^{\eta}\omega^{1-\eta})^{\beta}$.

* No spillovers: $\eta = 0$

* Spillovers: $\eta > 0$, with $\eta = 0.8$ for simulations.
Simulated changes in building quality and land value after Great Fire

<figure><img src="image_103.png" /><figcaption>Panel A. With cross-plot spillovers:
Building quality in burned and unburned areas</figcaption></figure>

<figure><img src="image_104.png" /></figure>

<figure><img src="image_105.png" /></figure>

Source: Hornbeck and Keniston (2017), figure 3
* Testable predictions:

* Fire increases plot land values in burned regions and land values converge over time.

* Fire increases land values and buildings in nearby unburned areas.
* Empirical strategy, plots 1876–1894:

$$Y_{it} = \alpha_t + \rho I_i^{\text{Fire}} + \beta_{1873} I_i^{\text{Fire}} \times I_t^{1873} + \beta_{1882} I_i^{\text{Fire}} \times I_t^{1882} \\ + \beta_{1894} I_i^{\text{Fire}} \times I_t^{1894} + \varepsilon_{it}$$

* $I_i^{\text{Fire}}$: indicator for within burned area.

* $\alpha_t$: year FE.

* Also include interaction between year FE and pre-Fire values.
Estimated impacts on land value in 1873 and 1882 by distance to fire

<figure><img src="image_106.png" /><figcaption>Panel A. Land value in 1873</figcaption></figure>

Distance in feet from the burned area boundary

<figure><img src="image_107.png" /><figcaption>Panel B. Land value in 1882</figcaption></figure>

Distance in feet from the burned area boundary

Source: Hornbeck and Keniston (2017), figure 4
Estimated impacts on building value in 1873 and 1882 by distance to fire

<figure><img src="image_108.png" /><figcaption>Panel C. Building value in 1873</figcaption></figure>

Distance in feet from the burned area boundary

<figure><img src="image_109.png" /><figcaption>Panel D. Building value in 1882</figcaption></figure>

Distance in feet from the burned area boundary

Source: Hornbeck and Keniston (2017), figure 4
Estimated impacts on building and land value in 1894 by distance to fire

<figure><img src="image_110.png" /><figcaption>Panel A. Land value in 1894</figcaption></figure>

Distance in feet from the burned area boundary

<figure><img src="image_111.png" /><figcaption>Panel B. Building value in 1894</figcaption></figure>

Distance in feet from the burned area boundary

Source: Hornbeck and Keniston (2017), figure 5
Cities and growth
* When GDP estimates nonexistent, urbanization or population density can be proxy for growth:

* Link between economic and population growth is key prediction of Malthusian model.

* Malthusian trap: technological progress absorbed by population growth. Ashraf and Galor (2011)
* When GDP estimates nonexistent, urbanization or population density can be proxy for growth:

* Link between economic and population growth is key prediction of Malthusian model.

* Malthusian trap: technological progress absorbed by population growth. Ashraf and Galor (2011)

* Alternative: local agricultural productivity.
Bairoch (1988) Dittmar (2011)

* Cost of supplying food to cities key for city size.

* Technological progress or fall in transportation cost translates in urbanization.
* Cities are central contributors to technological development.

* Direction of causality between urbanization and productivity growth (e.g., impact of new Swedish towns on local productivity).
  Carmeno and Enflo (2019)

* Some theoretical framework developed.
  Desmet and Rossi-Hansberg (2014) Desmet et al (2018)

* Trade-off between optimal city size and technological change (e.g., guilds as barrier to entry).
  Desmet and Parente (2014)
Topic 5
Economics history and culture
* Research questions involving culture often require historical perspective:

* How can we explain observed differences in culture?

* Mechanisms through which culture affects behavior?

* How does culture transmit across generations?

* When does culture persist over time? When doesn't it?

* What are the mechanisms of cultural change?

* How does culture emerge?

* Specific (and standard) empirical issues still apply.
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

* Explain contemporaneous distribution of trust in Africa: the slave trade. Nunn and Wantchekon (2011)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

* Explain contemporaneous distribution of trust in Africa: the slave trade. Nunn and Wantchekon (2011)

* Explain contemporaneous distribution of economic status in Africa: ancestral lifeways. Michalopoulos, Putterman and Weil (2019)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

* Explain contemporaneous distribution of trust in Africa: the slave trade. Nunn and Wantchekon (2011)

* Explain contemporaneous distribution of economic status in Africa: ancestral lifeways. Michalopoulos, Putterman and Weil (2019)

* Mechanisms of persistence of historical shocks: contemporaneous gender norms in Africa and the slave trade. Teso (2019)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

* Explain contemporaneous distribution of trust in Africa: the slave trade. Nunn and Wantchekon (2011)

* Explain contemporaneous distribution of economic status in Africa: ancestral lifeways. Michalopoulos, Putterman and Weil (2019)

* Mechanisms of persistence of historical shocks: contemporaneous gender norms in Africa and the slave trade. Teso (2019)

* Mechanisms of persistence of historical shocks: contemporaneous and past gender norms in France and WWI. Gay (2023)
* Focus on method to identify culture: the epidemiological approach.

* Main example: traditional gender roles and FLFP.

* Leave aside important aspects:

* Economic theory of preference formation and cultural transmission.
    Bisin and Verdier (2011)

* Interactions between culture and institutions.
    Alesina and Giuliano (2015)

* Then apply the method to economic history questions
Distribution of FLFP across Countries

<figure><img src="image_112.png" /></figure>

• FLFP 15–65 (2010–2014) across 187 countries ILO Estimates
Distribution of Attitudes across Countries

<figure><img src="image_113.png" /></figure>

* Share approves of “When jobs are scarce, men should have more right to a job than women” across 58 countries WVS, Wave 6, 2010–2014
Distribution of Attitudes across Countries

<figure><img src="image_114.png" /></figure>

* Share approves of "Being a housewife is just as fulfilling as working for pay" across 58 countries WVS, Wave 6, 2010–2014
Distribution of FLFP and Attitudes across Countries

<figure><img src="image_115.png" /></figure>

Source: WVS, ILO.
Correlation between Attitudes and FLFP

<figure><img src="image_116.png" /></figure>

* Values: share approves of “When jobs are scarce, men should have more right to a job than women”
Correlation between Attitudes and FLFP

<figure><img src="image_117.png" /></figure>

* Values: share that approves of "Being a housewife is just as fulfilling as working for pay"
FLFP and Attitudes Across Time in the U.S.

<figure><img src="image_118.png" /></figure>

* FLFP sample: white married women aged 30–49 in nonfarm IPUMS 1900–2016

* Share approves of “A married woman earning money in business or industry if she has a husband capable of supporting her” Gallup 1938 GSS 1972–1998
Holds across many dimensions e.g.:

* Gender roles and fertility, distribution of household tasks.

* Family ties and civic participation, geographical mobility.

* Thrift and saving rates.

* Belief in luck and redistribution.

* Trust and civic participation, shirking, financial participation.
* Are these correlations:

* Driven by systematic cultural differences?

* Driven by economic and institutional differences?

* Traditional view:

* Economics: explain $\Delta$ in behaviors by $\Delta$ in prices and income.

* Stigler and Becker (1977) De Gustibus Non Est Disputandum
* Is there credible empirical evidence that:

* Culture matters for economic behavior?

* Cultural change matters for changes in economic behavior?
* Is there credible empirical evidence that:

* Culture matters for economic behavior?

* Cultural change matters for changes in economic behavior?

* If culture matters, many “new” economic questions:

* Mechanisms through which culture affects behavior?

* How does culture transmit across generations?

* When does culture persist over time? When doesn't it?

* What are the mechanisms of cultural change?

* How does culture emerge?
Measuring culture
Culture is the integrated pattern of human knowledge, belief, and behavior that depends upon the capacity for learning and transmitting knowledge to succeeding generations, and the customary beliefs, social forms, and material traits of a racial, religious group, or social group; and the set of shared attitudes, values, goals, and practices that characterizes an institution or organization.

(Webster Dictionary)

* Broadly, culture is a body of shared knowledge and practices.
* Need practical definition for empirical analysis.

* Identification: more useful to think about *differences in culture*.

* Differences in culture are:

* Systematic variations in preferences and beliefs.

* Across time, space, and/or social groups.

* Most common criteria: space. But not always most relevant.

* No attempt to differentiate preferences, expectations, beliefs.
  *Bicchieri (2006, 2016)*
Alternative approach:

* Culture as a focal factor. Myerson (2009)

* Social structures characterized as a game with multiple equilibria.

* Culture is a mechanism of equilibrium selection and coordination.

* No need for differences in preferences.
## Alternative approach:

* Culture as a focal factor. Myerson (2009)

* Social structures characterized as a game with multiple equilibria.

* Culture is a mechanism of equilibrium selection and coordination.

* No need for differences in preferences.

* Related to the emergence of culture?

* Challenging to provide empirical evidence (analytical narratives).
* World-wide:

* World Values Survey: 6 waves, 1981–2014, up to 60 countries.

* International Social Survey: 4 waves, 1985–2016, up to 43 countries.

* Global Preferences Survey: 76 countries (2012). Falk et al. (2018)

* Regional:

* European Values Study: 4 waves, 1981–2009, up to 47 countries.

* Demographic and Health Surveys: 1985–2017, 90 countries.

* Afro Barometer (1999–2019), Latino Barometer (1995–2017), Asian Barometer (2001–2016).

* Country:

* United States: General Social Survey (1976–2017).

* France: DREES, ECAM, CREDOC...
* Trust.

* Gender roles.

* Time preference.

* Individualism vs collectivism.

* Family ties.

* Morality.

* Work and Poverty.
# Measurement Challenges: Data Availability

* Across countries.

* WVS: 60 countries in wave 6 (2010–2014).

* GPS: 76 countries, 90% of world population.

* Cultures as units matter.

* Across time.

* WVS: wave 1 (1981–1984), 10 countries.

* GSS: since 1976, but only for the U.S.

* Culture is a long-run process, changes over multiple generations.

* Within countries.

* WVS: wave 6 (2010–2014), average 13 regions per country.

* GPS: state/province of residence.

* Usually: no precise residence location, no birth location.
Data Availability across Countries

<figure><img src="image_119.png" /></figure>

* Share approves of "When jobs are scarce, men should have more right to a job than women" across 58 countries WVS, Wave 6, 2010–2014
# Measurement Challenges: Aggregation Issues

* Question level:

* "When jobs are scarce, men should have more right to a job than women"

* Agree: $1 \to 1$.

* Neither: $2 \to 0.5$.

* Disagree: $3 \to 0$.

* "Being a housewife is just as fulfilling as working for pay"

* Strongly agree: $1 \to 1$.

* Agree: $2 \to 0.66$.

* Disagree: $3 \to 0.33$.

* Strongly disagree: $4 \to 0$.

* Several issues with Likert scales.

* Index level: linear, PCA, ...?

* Country level: flat or sample weights?
* What is measured?

* Expectations, beliefs (1st, 2nd order), preferences, attitudes?

* GPS: cross-validated with experiments (but rare).

* Comparing cultures

* Do universal categories of culture exist?

* Cultural biases (ethnocentrism).
* Past behavior e.g. past FLFP

* Captures culture + economic and institutional conditions.

* Data comparability and coverage.

* (Deep) historical measures

* E.g. Pre-modern division of labor, language structures.

* Fixed over time.

* Measurement issues.

* Little within-country variation.

* Assumptions on historical processes (persistence, transmission).
* Criteria for measure:

* Consistent across time, at least across 3 generations.

* Consistent across space, especially within a country.

* Data availability.

* Represent local preferences and beliefs.

* Some ideas:

* Textual analysis of books and newspapers. Giorcelli et al. (2019)

* Legislative behaviors (voting, activities, speeches).
Culture and behavior
<figure><img src="image_120.png" /></figure>

* Values: share approves of "When jobs are scarce, men should have more right to a job than women." WVS, Wave 6, 2010–2014
* Outcome: FLFP 15–65 (2010–2014). ILO

* Cultural variable: approves of “When jobs are scarce, men should have more right to a job than women” WVS

* (Very rough) controls (more later): area, GDP, schooling, fertility, migration, population.

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">FLFP 15–65</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>WVS question (0–1)</td><td>-0.61***<br>[0.08]</td><td>-0.82***<br>[0.16]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>Yes</td></tr><tr><td>Countries</td><td>51</td><td>51</td></tr><tr><td>Adj. R2</td><td>0.477</td><td>0.502</td></tr></tbody></table>
Results lack credibility:

* Reverse causality.

* Endogeneity with economic incentives and institutional structures.

* Few countries available in surveys.

* No data for past attitudes.
* Identify culture by fixing economic and institutional factors.

Fernández (2011)
* Identify culture by fixing economic and institutional factors.

Fernández (2011)

* Compare individuals

* Within same institutional and economic environment.

* Whose preferences and beliefs might differ systematically.
* Identify culture by fixing economic and institutional factors.

Fernández (2011)

* Compare individuals

* Within same institutional and economic environment.

* Whose preferences and beliefs might differ systematically.

* Compare movers (1st, 2nd, higher order generations).

* (Testable) assumptions:

* Cultural beliefs persist across generations.

* Cultural beliefs vary systematically across groups (spatially defined?).

* Individuals in the same area face identical institutional environment.
* Origin culture might attenuate over time.

* Institutional environment might vary within host environment.

* Movers are a selected sample.
* Gender roles and married women LFP.

* Married women $i$ from country $k$ maximizes:

$$U(c, v_i) = u(c) - 1v_i$$

* $u(\cdot)$: strictly increasing concave utility function.

* $1$: indicator for working.

* $c$: household consumption.

* $v_i$: disutility from working.

$$c = w_{hk} + 1w_{fk}$$

* $w_F$: wife labor income if works.

* $w_h$: husband's income (always works).
# The Epidemiological Approach: A Simple Model

## Sources of heterogeneity:

* **Wages:**

* Identical and exogenous within countries.

* (Potentially) heterogeneous across countries and sex.

* **Disutility of work:**

* Drawn from country-specific distribution $G_k(m_k, \sigma)$.

* Varies across women within a country.

* Varies across countries.

* **Differences in culture:** differences in mean $m_k$.

* **Approximation:** country ≈ culture.
* $L_k$: FLFP in country k is given by

$$L_k = \Pr(v_k \le v_k^*) = G_k(v_k^*)$$
with $v_k^* = v^*(w_{hk}, w_{fk}) = u(w_{hk} + w_{fk}) - u(w_{hk})$

* Suppose $G \sim \mathcal{N}(m_k, \sigma^2)$. Then:

$$L_k = G_k(v_k^*) = \Phi\left(\frac{v_k^* - m_k}{\sigma}\right)$$
* Culture matters through $m_k$: $↑ m_k \Rightarrow ↓ L_k$.

$$ \frac{\partial L_k}{\partial m_k} = -\frac{1}{\sigma} \phi \left( \frac{v_k^* - m_k}{\sigma} \right) < 0 $$

* Institutional framework matters through wages and thus $v_k^*$.

$$ \frac{\partial L_k}{\partial w_{fk}} = \frac{1}{\sigma} \phi \left( \frac{v_k^* - m_k}{\sigma} \right) u' (w_{hk} + w_{fk}) > 0 $$

$$ \frac{\partial L_k}{\partial w_{hk}} = \frac{1}{\sigma} \phi \left( \frac{v_k^* - m_k}{\sigma} \right) [u'(w_{hk} + w_{fk}) - u'(w_{hk})] < 0 $$
* Women from different countries in same environment and same husbands:

* Same $w_{hi}$ and same $w_{fi}$.

* Same threshold $v^*$.

* But draw from different distributions $G_k$.

* (Assumption: culture persists for higher order generations of movers.)

* Proportion of women from (ancestry) k who work in country j is:

$$L_{kj} = \Phi \left( \frac{v_j^* - m_k}{\sigma} \right)$$

* Structural estimation approach left aside

Fernández (2013) Fogli and Veldkamp (2011)
Cultural differences $m_k$ not observable, so need a proxy.

* Measure of attitudes: measurement issues.

* Economic behaviors directly.

* Reference group behavior e.g ancestry FLFP.

* Captures culture + economic and institutional conditions.

* Leverage economic and institutional conditions are fixed (same $v^*$).

* Left with cultural component.

* (Relatively) easy to implement.

* (Deep) historical measures:

* Measurement challenges.

* Potentially (much more) meaningful for identification.
Further issues common to all measures of culture:

* Other (endogenous) sources of heterogeneity beyond culture.

* Proxy might capture inherited social capital.

* Which dimension of culture is captured?
* Case: FLFP of Immigrants to the United States.

* Fernández and Fogli (2009) Blau (2015)

* Data: ACS 2005–2017 IPUMS

* Sample selection:

* Born outside the U.S. from non US parents.

* Age: 30–49.

* Regular household, no farms, married spouse present.

* Born in single identified country. BPL

⇒ 330k women from 142 countries.
# Cultural Proxy: Country of Birth FLFP

* Data: International Labor Organization ILO

* Variables:

* FLFP or ratio FLFP to MLFP.

* Age bounds: 15+ or 15–65.

* Assignment: year or decade of migration (1956–2017).

* Country coverage: 7 countries missing (635 obs).

* Time coverage:

* ILO Estimates: 1990–2017. Consistent. Drop 50k obs (15%).

* ILO Yearly Indicators: 1950–2017. Not consistent.

* 15+: drop 89k obs (27%), 10k (3%) if interpolated.

* 15–65: drop 112k obs (34%), 16k (4%) if interpolated.
① Data quality vs data coverage.

② Controls and comparison groups.

③ Interpretation: magnitude and precision.

④ Clustering.

⑤ Sources of identification.

⑥ Outliers.

⑦ Choice of cultural proxy.

⑧ Choice of outcome.

⑨ Regression model.
These might be helpful:

* Gentzkow and Shapiro (2014) *Code and Data for the Social Sciences*

* tips4economists

* Beamer advice

* In general: document everything!

* Replication will be very important in the future. Maniadis, Tufano and List (2017)

* BTW: register to NEP mailing list.
# 1. Data Quality vs Data Coverage

* Cultural Proxy: FLFP 15+

* ILO Estimates (EST). 1990–2017, 50k missing.

* ILO Yearly Indicators, raw (YI). 1956–2017, 89k missing.

* ILO Yearly Indicators, interpolated (YII). 1956–2017, 10k missing.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Level of aggregation: State.

* No controls.

* Two-way clustering: State + COB.
# 1. Data Quality vs Data Coverage

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.48***<br>[0.05]</td><td>0.29***<br>[0.07]</td><td>0.27***<br>[0.08]</td></tr><tr><td>ILO variable</td><td>EST</td><td>YI</td><td>YII</td></tr><tr><td>Time coverage</td><td>1990-2017</td><td>1956-2017</td><td>1956-2017</td></tr><tr><td>COB coverage</td><td>135</td><td>132</td><td>133</td></tr><tr><td>Observations</td><td>280,003</td><td>240,333</td><td>320,044</td></tr></tbody></table>

* Attenuation bias:

* (1) → (2) = -0.19 (-40%)

* (1) → (3) = -0.21 (-44%)

* But regression samples not comparable.