# Global Economic History

Victor Gay

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Victor Gay. Global Economic History. Master. Global Economic History, University of Michigan, Ann Arbor, Michigan, United States. 2024, 1488 p. hal-04650773

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Global Economic History

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

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Toulouse
School of
Economics
① What is economic history? (pp. 3–95)

② Modern economic growth in historical perspective (pp. 96–146)

* Historical growth accounting (pp. 147–214)

* The Great Divergence (pp. 215–255)

* Wages and living standards (pp. 256–305)

* Patterns of long-run growth in global perspective (pp. 306–579)

* Ancient economic history (pp. 580–684)

③ Factors of growth: explaining the Great Divergence

* Explaining the Great Divergence (pp. 685–766)

* Directed technical change (pp. 767–863)

* Institutions (pp. 864–1,151)

* Culture (pp. 1,152–1,250)

* Geography (pp. 1,251–1,322)

④ Issues with modern economic growth

* Pollution (pp. 1,323–1,385)

* Pandemics in historical perspective (pp. 1,386–1,488)
What Is Economic History?

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

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Toulouse
School of
Economics
Does the past have useful economics?
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)

* Methodological issues in economic history. Blum and Colvin (2018)
## 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 [sic] 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
performance 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 [sic] 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
performance 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.
* Greatest accomplishment for empirical economics and economic history.

* 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.
# 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)

* Methodological issues in economic history. Blum and Colvin (2018)
## 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 a subject like physics, true for all time, or are its laws historically conditioned?
Is economics a subject 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 a subject 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)

* Methodological issues in economic history. Blum and Colvin (2018)
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).
* 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).

* 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).

* 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 2022).
* 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.

* Historical roots of economic development. Nunn (2020)

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

* Historical roots of economic development. Nunn (2020)

* 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.

* Historical roots of economic development. Nunn (2020)

* 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.

* Historical roots of economic development. Nunn (2020)

* 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 (2021)

* Methodological issues in economic history. Blum and Colvin (2018)
* Economics versus history

* Measurement and historical data

* Econometric identification

* Geographic Information Systems (GIS)
Colvin and Wagenaar (2018)

* Interdisciplinary field: between economics and history.
Colvin and Wagenaar (2018)

* Interdisciplinary field: between economics and history.

* Traditional history:

* Focus on the particular, explain individual events, outliers.

* No counterfactual thinking.

* Attributes cause and effect through timing.
## Colvin and Wagenaar (2018)

* Interdisciplinary field: between economics and history.

* Traditional history:

* Focus on the particular, explain individual events, outliers.

* No counterfactual thinking.

* Attributes cause and effect through timing.

* Economics:

* Focus on generalizations, explain trends, means and variances.

* Counterfactual approach: reality is a draw from distribution.

* Disentangle causes from effects.
* Bad economics is purely deductive:

* Reasoning from premises to reach logical conclusions.

* Might rely on stylized facts, but might generate self-referential systems with little explanatory power.
* Bad economics is purely deductive:

* Reasoning from premises to reach logical conclusions.

* Might rely on stylized facts, but might generate self-referential systems with little explanatory power.

* Bad history is purely inductive.

* Use observation to draw arguments based on evidence.

* Might select those archival evidence to confirm theory
* Bad economics is purely deductive:

* Reasoning from premises to reach logical conclusions.

* Might rely on stylized facts, but might generate self-referential systems with little explanatory power.

* Bad history is purely inductive.

* Use observation to draw arguments based on evidence.

* Might select those archival evidence to confirm theory

* Good economic history uses abduction:

* Surprizing observation (against prior).

* Build hypothesis.

* Test hypothesis and reject it.

⇒ Back and forth between theory and empirics.
Blum (2018)

* Measurement and source criticism is fundamental:

* Right measure and level of aggregation.

* Data generated how, by whom, for what, with which representation system?

* Inputting data enables knowledge of hidden regularities.

* Measurement error invalidates any identification strategy.
Blum (2018)

* Measurement and source criticism is fundamental:

* Right measure and level of aggregation.

* Data generated how, by whom, for what, with which representation system?

* Inputting data enables knowledge of hidden regularities.

* Measurement error invalidates any identification strategy.

* Mismeasurement and bias can be exploited:

* Difficult to measure populations' numeric skills.

* "Age heaping" method.

* Frequency of rounding (0 or 5) age when self-reporting.
## Blum and Dimicio (2018)

* Economic history uses standard tools of econometrics.

* Identifying correlation:

* Controlling for observables.

* Multivariate OLS regressions.

* But measurement error, reverse causality, omitted variable bias.
Blum and Dimicio (2018)

* Economic history uses standard tools of econometrics.

* Identifying correlation:

* Controlling for observables.

* Multivariate OLS regressions.

* But measurement error, reverse causality, omitted variable bias.

* Identifying causality:

* Empirical design: exogenous variation in treatment variable.

* Difference-in-differences.

* Instrumental variables (included in the narrative). Caicedo (2020)

* Regression discontinuity designs.
* Deep institutional knowledge essential for credibility of identification.

* External validity issues: LATEs.

* Trade-off between validity and importance.

* Ask questions first, look for identification after.
Johnson (2018)

* GIS as a tool for new historical data:

* Matching modern datasets with historical units.

* Suitable for time-invariant characteristics: topography, agricultural potential, climate. Nunn and Puga (2012)

* Spatially disaggregated modern data (for persistence studies): night light intensity. Michalopoulos and Papaioannou (2018)
Johnson (2018)

* GIS as a tool for new historical data:

* Matching modern datasets with historical units.

* Suitable for time-invariant characteristics: topography, agricultural potential, climate. Nunn and Puga (2012)

* Spatially disaggregated modern data (for persistence studies): night light intensity. Michalopoulos and Papaioannou (2018)

* GIS as a tool for matching historical data:

* Match heterogeneous geolocated datasets. Dittmar (2011)

* Georeferencing historical maps.
* GIS as a tool for new analyzes:

* Spatial regression discontinuity designs.

* Spatial networks: optimal paths and market access.
* GIS as a tool for new analyzes:

* Spatial regression discontinuity designs.

* Spatial networks: optimal paths and market access.

* Using GIS methods:

* Softwares: QGIS (free) and ArcGIS (pay).

* Coding: python.
When and how is economic history valuable to better
understand the present?
Modern Economic Growth
in Historical Perspective

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_8.png" /></figure>

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

<figure><img src="image_10.png" /></figure>

Toulouse
School of
Economics
Why are some countries so much richer than others?
* Current cross-country differences in income

* GDP and welfare

* Economic growth and income differences
Current cross-country differences in income
## 2017 PPP-adjusted GDP in bil. 2011 US $

<figure><img src="image_11.png" /></figure>
2017 PPP-adjusted GDP per Capita in thousands. 2011 US $

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Distribution of Countries: GDP per Capita</figcaption></figure>

Source: Penn World Table 9.1, 110 countries.
<figure><img src="image_14.png" /><figcaption>Distribution of Countries: Log GDP per Capita</figcaption></figure>

Source: Penn World Table 9.1, 110 countries.
<figure><img src="image_15.png" /><figcaption>Population-Weighted Distribution of Countries: Log GDP per Capita</figcaption></figure>

Source: Penn World Table 9.1, 110 countries.
<figure><img src="image_16.png" /><figcaption>Distribution of Countries: GDP per Capita</figcaption></figure>

Source: Maddison Project Database 2018, 86 countries.
<figure><img src="image_17.png" /><figcaption>Distribution of Countries: Log GDP per Capita</figcaption></figure>

Source: Maddison Project Database 2018, 86 countries.
* Best cross-country GDP modern data: Penn World Tables 10.0.

* Reference paper: Feenstra, Inklaar and Timmer (2015).

* GDP, consumption, TFP measures.

* PPP-adjusted, constant prices (2011 US $) ⇒ comparability.

* 183 countries, 1950–2019, yearly.
* Best cross-country pre-1950 GDP data source: Maddison Project.

* Reference paper: Bolt, Inklaar, de Jong and van Zanden (2018).

* New version: GDP per capita based on PWT methodology.

* 80 countries pre-1950, starting 1800s (even before).

* Lower data availability than PWT.
GDP and welfare
## Definition (Gross Domestic Product)

GDP is the market value of final goods and services newly produced in a country during a given period of time
## Definition (Gross Domestic Product)

GDP is the market value of final goods and services newly produced in a country during a given period of time

* GDP is the most commonly used indicator of economic activity.

* GDP is measured in a currency at **current** prices.

* GDP measures **final** goods and services.

* GDP measures goods newly **produced** during the period considered.
## ① Product approach

* Highlights **value-added** of domestic output.

* Most common definition, used in economic history.
## ① Product approach

* Highlights **value-added** of domestic output.

* Most common definition, used in economic history.

## ② Expenditure Approach

* Highlights **spendings** on domestic goods and services.

* Most used measure today.
## ① Product approach

* Highlights **value-added** of domestic output.

* Most common definition, used in economic history.

## ② Expenditure Approach

* Highlights **spendings** on domestic goods and services.

* Most used measure today.

## ③ Income Approach

* Highlights **income** earned by factors operating domestically.
## ① Product approach

* Highlights **value-added** of domestic output.

* Most common definition, used in economic history.

## ② Expenditure Approach

* Highlights **spendings** on domestic goods and services.

* Most used measure today.

## ③ Income Approach

* Highlights **income** earned by factors operating domestically.

⇒ Production = Expenditure = Income.
$$ \underbrace{Y}_{\text{Final Product}} = \frac{C + I + G + (X - M)}{\underbrace{\text{Expenditure on Final Goods and Services}}_{\text{Expenditure on Final Goods and Services}}} $$

* Y: final product (or total income earned by domestic factors).

* C: consumption spendings on goods and services.

* I: investment spendings on capital (and changes in inventories)

* G: government expenditures on goods and services.

* X - M: net exports of goods and services.
A distorted picture of reality
① GDP per capita matters.
① GDP per capita matters.

② GDP is a constructed measure, subject to revisions.
① GDP per capita matters.

② GDP is a constructed measure, subject to revisions.

③ Many things that contribute to wealth are not measured:
A distorted picture of reality

① GDP per capita matters.

② GDP is a constructed measure, subject to revisions.

③ Many things that contribute to wealth are not measured:

* Goods and services not sold on a market: e.g. home production, child care, clean air.

* Illegal activities: e.g. drugs, prostitution.

* Natural resource depletion.

* Welfare: e.g. leisure, health, inequality, happiness.

* Digital goods.

* Changes in quality.
<figure><img src="image_18.png" /><figcaption>Income per Capita and Consumption per Capita, 2017</figcaption></figure>

Source: Penn World Table 9.1, 182 countries.
## Jones and Klenow (2016)

* Compute a new welfare measure that combines consumption, leisure, inequality and mortality.
## Jones and Klenow (2016)

* Compute a new welfare measure that combines consumption, leisure, inequality and mortality.

<table><caption>France vs USA in 2005</caption><thead><tr><td>Indicator</td><td>France</td><td>USA</td></tr></thead><tbody><tr><td>GDP per Capita</td><td>67</td><td>100</td></tr><tr><td>Consumption per Capita</td><td>60</td><td>100</td></tr><tr><td>Life Expectancy</td><td>80</td><td>77</td></tr><tr><td>Leisure (h. work)</td><td>535</td><td>877</td></tr><tr><td>Inequality (Gini)</td><td>0.261</td><td>0.369</td></tr><tr><td>Welfare</td><td>92</td><td>100</td></tr></tbody></table>
Welfare and income are correlated at 98%

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /><figcaption>Welfare differs from income by about 35%</figcaption></figure>
Adhami et al. (2023)

* Social welfare perspective: total population matters too!

* Japan 1960–2019: consumption per capita × 6, population × 1.3.

* Mexico 1960–2019: consumption per capita × 3, population × 3.

* Reconsider growth using a consumption-equivalent metric based on total utilitarian social welfare.

⇒ Put “humans” into a Human Development Index.
* Total welfare is $W(N_t, c_t) = N_t \cdot u(c_t)$.

* $N_t$: number of (identical) people.

* $c_t$: (representative) consumption per capita.

* $u(\cdot)$: flow utility function.

* $u(\cdot)$ is positive with diminishing returns ($u > 0, u' > 0, u'' < 0$).
* Calculate a consumption-equivalent of welfare growth based on slope of social indifference curve.

* Get $v(c)$, value of having one more person alive for one period, i.e., 1pp population growth is worth $v(c)$ pp of consumption growth.

* $v(c) > 1$ because of “consumer surplus” associated with diminishing marginal utility.

* Weight $v(c)$ is the “value of a year of life” in consumption per person.

⇒ As living standards increase, “life” becomes increasingly valuable.
* Example in U.S. 2006:

* Value of statistical life = $7.4m.

* Consumption per capita per year = $38k.

* Given life expectancy and assuming lof utility: $v(c_{US,2006}) \approx 4.87$.

* A year of life in US 2006 is valued at 5 years worth of consumption per person.
<figure><img src="image_21.png" /><figcaption>$v(c)$ over time in the US</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>$v(c)$ across countries in 2019</figcaption></figure>
$$CE = v(c_t) \cdot g_{Nt} + g_{ct}$$

* CE: consumption equivalent.

* g_N: population growth.

* g_c: consumption growth.

* v(c): weight on population growth or value of a year of life measured in years of per capita consumption.
Overview of results from 1960 to 2019

<table><thead><tr><th></th><th>Unweighted</th><th>Pop Weighted</th></tr></thead><tbody><tr><td>CE-welfare growth, g<sub>λ</sub></td><td>6.2%</td><td>5.9%</td></tr><tr><td>Population term, v(c)g<sub>N</sub></td><td>4.1%</td><td>3.1%</td></tr><tr><td>Consumption term, g<sub>c</sub></td><td>2.1%</td><td>2.8%</td></tr><tr><td>Population growth, g<sub>N</sub></td><td>1.8%</td><td>1.6%</td></tr><tr><td>Value of life, v(c)</td><td>2.7</td><td>2.3</td></tr><tr><td>Pop share of CE-welfare growth</td><td>66%</td><td>51%</td></tr></tbody></table>
<figure><img src="image_23.png" /><figcaption>Welfare growth in selected countries, 1960–2019</figcaption></figure>
## Changing perspectives on who is growing rapidly

<figure><img src="image_24.png" /></figure>
<figure><img src="image_25.png" /><figcaption>Growth in China by decade</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>Growth in Sub-saharan Africa by decade</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>Cumulative growth in “the world”, 1500–2018</figcaption></figure>

* Consumption per capita ×20 ($g_c$ = 0.6% per year).

* Welfare × 3,700 ($g_{CE}$ = 1.6% per year).
Economic growth and income differences
* Growth rates explain large income differences.
* Growth rates explain large income differences.

* Let g: growth rate, T = time, GDP₀ = 1. Then

$$GDP_T = (1 + g)^T$$

* Suppose gA = 2% and gB = 0%.

* After 100 years, A 7 times richer than B.

* After 200 years, A 52 times richer than B.
* Growth rates explain large income differences.

* Let $g$: growth rate, $T$ = time, $GDP_0$ = 1. Then

$$GDP_T = (1 + g)^T$$

* Suppose $g_A$ = 2% and $g_B$ = 0%.

* After 100 years, A 7 times richer than B.

* After 200 years, A 52 times richer than B.

* How long to:

* double GDP? $log 2 / log(1 + g)$. If $g$ = 2%, then 35 years.

* decuple GDP? $log 10 / log(1 + g)$. If $g$ = 2%, then 116 years.
<figure><img src="image_28.png" /><figcaption>Distribution of GDP per Capita Growth Rates</figcaption></figure>

Source: Penn World Table 9.1
<figure><img src="image_29.png" /><figcaption>Evolution of Income per Capita across Countries</figcaption></figure>

Source: Maddison Project Database 2018.
<figure><img src="image_30.png" /><figcaption>Evolution of Income per Capita across Continents</figcaption></figure>

Source: Maddison Project Database 2018.
Historical Growth Accounting

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_31.png" /></figure>

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

<figure><img src="image_33.png" /></figure>

Toulouse
School of
Economics
How can we measure historical GDP?
* Potential value of growth accounting in economic history:

* Benchmarking growth performance over the long run.

* Quantification of contributions to growth of particular sectors or new technology.

* Understand nature of TFP growth.

* Potential weaknesses of growth accounting in economic history:

* Highly sensitive to methodology and data quality.

* Does not tell anything about causality from factor input growth to TFP growth.

⇒ Prerequisite to testing hypotheses about factors of long-run growth.
* Central question: relationship between inputs and outputs.

* Typically, Cobb-Douglas production function:

$$Y = AL^{1-\alpha}K^{\alpha}$$

* $\alpha$ and $1 - \alpha$: output elasticities of capital and labor.

* A: total factor productivity (TFP).

* Diminishing returns to factor accumulation.
* Basic growth accounting equation in growth rates:

$$ \Delta \log (Y/L) = \alpha \Delta \log (K/L) + \Delta \log A $$

* Factor share $\alpha$ constant $\approx 1/3$.

* Issues measuring $L$ and $K$, but feasible.

* Once estimate $Y$, get estimate of $A$.
* Basic growth accounting equation in growth rates:

$$ \Delta \log (Y/L) = \alpha \Delta \log (K/L) + \Delta \log A $$

* Factor share $\alpha$ constant $\approx 1/3$.

* Issues measuring $L$ and $K$, but feasible.

* Once estimate $Y$, get estimate of $A$.

* For comparisons across time and space: proper price levels.

⇒ How to estimate $Y$ and convert into PPP?
* Maddison's (2001) *The World Economy: A Millennial Perspective*.

* Result of 50 years of research.

* Maddison (2010) Online database.

* Maddison Project: extends Maddison work. Bolt et al. (2018)
* Official estimates by national statistical offices (1870–1950).

* Historical estimates based on same methods (pre-1870).

* Historical estimates based on proxy variables (pre-1870).

* “Guesstimates.”
* Most common for pre-1870: output approach.

* Broadberry et al. (2015) *British Economic Growth, 1270–1870*

* Sectors estimated separately with auxiliary data for 1270–1700:

* Agricultural output.

* Industrial output.

* Service sector output.
* Three databases across different periods:

* 1250–1500: Medieval Accounts Database. Manorial accounts for land use, crops, animals, livestock products. Campbell (2000, 2007)

* 1550–1750: Early Modern Probate Inventories Database. Animal stocking densities and indirect estimates of arable yields. Overton (1991, 2000) Overton et al. (2004)

* 1700–1800: Modern Farm Accounts Database. Sample of farmers accounts. Turner et al. (2001)
<figure><img src="image_34.png" /><figcaption>Number of sampled farm enterprises per year</figcaption></figure>

Source: Broadberry et al. (2015)
* Seigniorial domain accounts: crops, animals, livestock products, capital equipment.

* Biases in manorial accounts:

* Bias toward large ecclesiastical estates.

* Uneven geographical coverage: bias to the south and east.

* Non-seigniorial sector large (75%) and not represented.

* Solutions:

* Weighting scheme for regional imbalances.

* Auxiliary information for non-seigniorial sector (tax and tithe returns from peasant holdings) showing strong correlation in crop yields across sectors.
Probate inventories (1550–1750)

* Comparable information to manorial accounts.

* Available at death of farmer, with quantities and values of crops stored on farm and livestock.

* Biases in the Probate Inventories Database:

* Omissions of large parts of northern, midland, southern England (8 of 43 counties, most in the east).

* One-off observation occasioned by deaths of farmers.

* Crop acreages available but not grain yield, so assumptions needed.

* Under representation of very large and very small farms.
* Paradoxically, least well-documented period.

* Sample of farms accounts starting in the 1720s.

* Biases in farmers accounts:

* Less standardized than late-medieval period.

* No information on land acreage or crops sown and harvested, not always animals.

* Uneven coverage over time and space (though more even than other sources).

* Selection of farmers who keep proper accounts.
* Agricultural output = acreage per crop × yield per acre.

* Deduce grain used as seed and as fodder for working animals.
# Britain's agricultural output

* Agricultural output = acreage per crop × yield per acre.

* Deduce grain used as seed and as fodder for working animals.

* Agricultural land use:

* Start with survey of farmland in 1871: 43% of land for agriculture.

* Account for population changes and density, changes in potential arable land through drainage of wetland, deserted medieval villages.

* Get 39% of land area for agriculture in 1290.

* Interpolate in between.
# Britain's agricultural output

* Agricultural output = acreage per crop × yield per acre.

* Deduce grain used as seed and as fodder for working animals.

* Agricultural land use:

* Start with survey of farmland in 1871: 43% of land for agriculture.

* Account for population changes and density, changes in potential arable land through drainage of wetland, deserted medieval villages.

* Get 39% of land area for agriculture in 1290.

* Interpolate in between.

* Then allocate arable land between crops from the three databases (wheat, rye, barley, oats, potatoes).
# Britain's agricultural output

## Changes in the distribution of arable land (1290–1871)

<table><thead><tr><th>Region</th><th>Estimated arable acreage, 1290</th><th>Arable as % total area, 1290</th><th>Recorded arable acreage, 1871</th><th>Arable as % total area, 1871</th><th>Net change in arable area, 1290-1871</th><th>Net change in % arable, 1290-1871</th></tr></thead><tbody><tr><td>Eastern counties</td><td>2,810,950</td><td>58.8</td><td>3,056,568</td><td>63.0</td><td>245,618</td><td>4.2</td></tr><tr><td>NE midlands</td><td>1,200,070</td><td>49.0</td><td>952,758</td><td>39.2</td><td>-247,312</td><td>-9.8</td></tr><tr><td>Northeast</td><td>1,622,420</td><td>27.6</td><td>1,950,640</td><td>34.1</td><td>328,220</td><td>6.5</td></tr><tr><td>Northwest</td><td>708,220</td><td>17.4</td><td>1,016,285</td><td>24.5</td><td>308,065</td><td>7.1</td></tr><tr><td>Southeast</td><td>3,268,150</td><td>48.4</td><td>3,516,531</td><td>51.2</td><td>248,381</td><td>2.8</td></tr><tr><td>Southwest</td><td>639,370</td><td>25.0</td><td>1,031,253</td><td>40.6</td><td>391,883</td><td>15.6</td></tr><tr><td>SW midlands</td><td>1,506,550</td><td>44.9</td><td>1,311,922</td><td>39.0</td><td>-194,628</td><td>-5.9</td></tr><tr><td>West midlands</td><td>994,270</td><td>40.2</td><td>1,007,413</td><td>41.9</td><td>13,143</td><td>1.7</td></tr><tr><td>ENGLAND</td><td>12,750,000</td><td>39.4</td><td>13,843,370</td><td>42.8</td><td>1,093,370</td><td>3.4</td></tr></tbody></table>

Source: Broadberry et al. (2015)
Britain's agricultural output

Composition of arable land use (1290–1871, millions of acres)

<table><thead><tr><th>Year</th><th>Wheat</th><th>Rye/maslin</th><th>Barley/dredge</th><th>Oats</th><th>Pulses</th><th>Potatoes</th><th>Other crops</th><th>Total sown</th><th>Fallow arable</th><th>Total arable</th></tr></thead><tbody><tr><td>1270</td><td>2.21</td><td>0.72</td><td>1.23</td><td>2.94</td><td>0.29</td><td>0.00</td><td>0.00</td><td>7.40</td><td>5.13</td><td>12.52</td></tr><tr><td>1300</td><td>2.68</td><td>0.60</td><td>1.27</td><td>3.16</td><td>0.45</td><td>0.00</td><td>0.00</td><td>8.16</td><td>4.56</td><td>12.72</td></tr><tr><td>1380</td><td>1.83</td><td>0.36</td><td>1.22</td><td>1.87</td><td>0.47</td><td>0.00</td><td>0.00</td><td>5.75</td><td>3.89</td><td>9.64</td></tr><tr><td>1420</td><td>1.61</td><td>0.32</td><td>1.17</td><td>1.66</td><td>0.45</td><td>0.00</td><td>0.00</td><td>5.21</td><td>3.53</td><td>8.75</td></tr><tr><td>1450</td><td>1.53</td><td>0.31</td><td>1.15</td><td>1.59</td><td>0.44</td><td>0.00</td><td>0.00</td><td>5.03</td><td>3.41</td><td>8.44</td></tr><tr><td>1500</td><td>1.58</td><td>0.37</td><td>1.19</td><td>1.56</td><td>0.47</td><td>0.00</td><td>0.10</td><td>5.26</td><td>3.24</td><td>8.50</td></tr><tr><td>1600</td><td>1.85</td><td>0.77</td><td>1.44</td><td>1.32</td><td>0.61</td><td>0.00</td><td>0.72</td><td>6.72</td><td>2.16</td><td>8.87</td></tr><tr><td>1650</td><td>2.00</td><td>0.39</td><td>1.86</td><td>1.13</td><td>1.02</td><td>0.00</td><td>1.36</td><td>7.74</td><td>1.88</td><td>9.63</td></tr><tr><td>1700</td><td>1.99</td><td>0.42</td><td>1.82</td><td>1.15</td><td>0.98</td><td>0.00</td><td>1.30</td><td>7.64</td><td>1.91</td><td>9.56</td></tr><tr><td>1750</td><td>1.95</td><td>0.06</td><td>1.50</td><td>1.82</td><td>0.98</td><td>0.08</td><td>2.53</td><td>8.92</td><td>1.59</td><td>10.51</td></tr><tr><td>1800</td><td>2.51</td><td>0.06</td><td>1.46</td><td>1.97</td><td>0.83</td><td>0.17</td><td>2.90</td><td>9.91</td><td>1.28</td><td>11.19</td></tr><tr><td>1830</td><td>2.12</td><td>0.06</td><td>1.81</td><td>1.27</td><td>0.63</td><td>0.26</td><td>4.46</td><td>10.62</td><td>1.30</td><td>11.91</td></tr><tr><td>1871</td><td>3.31</td><td>0.06</td><td>1.96</td><td>1.45</td><td>0.90</td><td>0.39</td><td>5.28</td><td>13.35</td><td>0.48</td><td>13.84</td></tr></tbody></table>

Source: Broadberry et al. (2015)
* Grain yield per acre and crop, net of seed sown.

* Use regression analysis:

$$ \ln(\text{Yield}_{it}) = \alpha + \beta_i + \gamma_j + \delta_t + \varepsilon_{it} $$

* Yield$_{it}$: yield of farm i in year t.

* β$_{i}$: location fixed effect.

* γ$_{i}$: region fixed effect.

* δ$_{i}$: year fixed effect.

* Get index of national trend.

* Absolute levels obtained by using regional shares of sown areas for each crop.
# Britain's agricultural output

English weighted national average gross crop yields per acre (bushels, log scale)

<figure><img src="image_35.png" /></figure>

Source: Broadberry et al. (2015)
# Britain's agricultural output

<table><caption>Total arable output net of seed and animal consumption (million bushels)</caption><thead><tr><th>Decade</th><th>Wheat<br>(m. bus.)</th><th>Rye<br>(m. bus.)</th><th>Barley<br>(m. bus.)</th><th>Oats<br>(m. bus.)</th><th>Pulses<br>(m. bus.)</th><th>Potatoes<br>(m. bus.)</th></tr></thead><tbody><tr><td>1270s</td><td>18.85</td><td>8.94</td><td>14.44</td><td>20.45</td><td>0.44</td><td></td></tr><tr><td>1300s</td><td>20.88</td><td>5.95</td><td>14.91</td><td>19.12</td><td>1.43</td><td></td></tr><tr><td>1350s</td><td>12.02</td><td>2.72</td><td>10.91</td><td>8.93</td><td>0.95</td><td></td></tr><tr><td>1400s</td><td>10.35</td><td>2.02</td><td>12.56</td><td>7.29</td><td>0.98</td><td></td></tr><tr><td>1450s</td><td>7.69</td><td>2.80</td><td>9.69</td><td>8.58</td><td>0.82</td><td></td></tr><tr><td>1500s</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td></td></tr><tr><td>1550s</td><td>17.08</td><td>3.83</td><td>11.82</td><td>8.14</td><td>1.74</td><td>ND</td></tr><tr><td>1600s</td><td>20.70</td><td>7.85</td><td>18.59</td><td>8.44</td><td>4.01</td><td>ND</td></tr><tr><td>1650s</td><td>27.01</td><td>3.70</td><td>33.50</td><td>6.14</td><td>6.53</td><td>ND</td></tr><tr><td>1700s</td><td>27.94</td><td>6.70</td><td>35.20</td><td>5.70</td><td>8.25</td><td>1.27</td></tr><tr><td>1750s</td><td>31.48</td><td>1.51</td><td>39.67</td><td>13.03</td><td>9.03</td><td>13.56</td></tr><tr><td>1800s</td><td>46.32</td><td>1.36</td><td>42.67</td><td>14.06</td><td>11.07</td><td>26.70</td></tr><tr><td>1850s</td><td>73.69</td><td>1.09</td><td>58.23</td><td>15.93</td><td>9.57</td><td>44.79</td></tr><tr><td>1860s</td><td>86.07</td><td>0.98</td><td>57.00</td><td>16.33</td><td>11.42</td><td>47.72</td></tr></tbody></table>

Source: Broadberry et al. (2015)
* Livestock farming is a function of:

* Number of non-working livestock.

* Proportion producing milk, meat, hides, skins, wool, and tallow.

* Respective yields per animal.

* Derived from databases and applying regional weighting scheme.

* Same method: regression to get aggregate trend, then weighting per region.
<figure><img src="image_36.png" /><figcaption>Number of non-working livestock (millions, log scale)</figcaption></figure>

Source: Broadberry et al. (2015)
Percentages of the animal stock producing specific livestock products

<table><thead><tr><th rowspan="2">Year</th><th colspan="6">% of animals producing</th></tr><tr><th>Milk</th><th>Beef</th><th>Veal</th><th>Mutton</th><th>Pork</th><th>Wool</th></tr></thead><tbody><tr><td>1300</td><td>90</td><td>15</td><td>14.1</td><td>26</td><td>49</td><td>90</td></tr><tr><td>1700</td><td>90</td><td>25</td><td>21.1</td><td>26</td><td>49</td><td>90</td></tr><tr><td>1850</td><td>90</td><td>33</td><td>25.0</td><td>40</td><td>100</td><td>80</td></tr></tbody></table>

Source: Broadberry et al. (2015)
# Britain's agricultural output

## Milk, meat, and wool yields per animal

<table><thead><tr><th>Decade</th><th>Milk (gals)</th><th>Beef (lbs)</th><th>Veal (lbs)</th><th>Mutton (lbs)</th><th>Pork (lbs)</th><th>Wool (lbs)</th></tr></thead><tbody><tr><td>1270s</td><td>100.00</td><td>168.00</td><td>29.00</td><td>22.00</td><td>64.00</td><td>1.63</td></tr><tr><td>1300s</td><td>100.96</td><td>169.26</td><td>29.22</td><td>22.14</td><td>64.11</td><td>1.48</td></tr><tr><td>1350s</td><td>112.27</td><td>183.91</td><td>31.79</td><td>23.81</td><td>65.36</td><td>1.81</td></tr><tr><td>1400s</td><td>124.83</td><td>199.82</td><td>34.59</td><td>25.60</td><td>66.64</td><td>1.49</td></tr><tr><td>1450s</td><td>138.81</td><td>217.11</td><td>37.63</td><td>27.52</td><td>67.94</td><td>1.24</td></tr><tr><td>1500s</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td></tr><tr><td>1550s</td><td>172.35</td><td>257.50</td><td>44.74</td><td>31.96</td><td>70.62</td><td>1.64</td></tr><tr><td>1600s</td><td>200.66</td><td>294.44</td><td>51.22</td><td>36.18</td><td>72.00</td><td>1.88</td></tr><tr><td>1650s</td><td>233.63</td><td>336.68</td><td>58.63</td><td>40.97</td><td>75.85</td><td>2.17</td></tr><tr><td>1700s</td><td>272.01</td><td>384.98</td><td>67.12</td><td>46.39</td><td>86.56</td><td>2.51</td></tr><tr><td>1750s</td><td>316.69</td><td>440.22</td><td>76.84</td><td>52.53</td><td>98.78</td><td>2.91</td></tr><tr><td>1800s</td><td>368.72</td><td>503.37</td><td>87.96</td><td>59.49</td><td>112.72</td><td>3.38</td></tr><tr><td>1850s</td><td>429.29</td><td>575.59</td><td>100.69</td><td>67.36</td><td>128.63</td><td>3.92</td></tr><tr><td>1860s</td><td>443.90</td><td>592.82</td><td>103.73</td><td>69.22</td><td>132.42</td><td>4.05</td></tr></tbody></table>

Source: Broadberry et al. (2015)
# Britain's agricultural output

## Total outputs of milk, meat, wool, hides, and hay

<table><thead><tr><th>Decade</th><th>Milk<br>(m. gals)</th><th>Beef<br>(m. lbs)</th><th>Veal<br>(m. lbs)</th><th>Mutton<br>(m. lbs)</th><th>Pork<br>(m. lbs)</th><th>Wool<br>(m. lbs)</th><th>Hides<br>(m. lbs)</th><th>Hay<br>(m. tons)</th></tr></thead><tbody><tr><td>1270s</td><td>42.09</td><td>10.57</td><td>1.98</td><td>81.33</td><td>26.13</td><td>20.84</td><td>6.29</td><td>0.12</td></tr><tr><td>1300s</td><td>54.45</td><td>13.72</td><td>2.63</td><td>90.49</td><td>30.82</td><td>20.89</td><td>7.34</td><td>0.11</td></tr><tr><td>1350s</td><td>51.46</td><td>13.52</td><td>2.56</td><td>94.49</td><td>21.33</td><td>24.82</td><td>7.36</td><td>0.12</td></tr><tr><td>1400s</td><td>45.25</td><td>12.39</td><td>2.32</td><td>75.11</td><td>12.38</td><td>15.13</td><td>6.32</td><td>0.10</td></tr><tr><td>1450s</td><td>44.92</td><td>12.83</td><td>2.37</td><td>83.90</td><td>14.06</td><td>13.11</td><td>6.59</td><td>0.09</td></tr><tr><td>1500s</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td></tr><tr><td>1550s</td><td>63.94</td><td>19.85</td><td>3.58</td><td>79.32</td><td>31.39</td><td>14.08</td><td>7.54</td><td>0.15</td></tr><tr><td>1600s</td><td>58.56</td><td>19.06</td><td>3.40</td><td>157.49</td><td>33.51</td><td>28.34</td><td>9.90</td><td>0.24</td></tr><tr><td>1650s</td><td>72.52</td><td>24.83</td><td>4.35</td><td>130.85</td><td>31.14</td><td>23.95</td><td>10.51</td><td>0.29</td></tr><tr><td>1700s</td><td>59.10</td><td>21.16</td><td>3.67</td><td>211.92</td><td>39.93</td><td>39.09</td><td>13.12</td><td>0.34</td></tr><tr><td>1750s</td><td>163.19</td><td>62.94</td><td>10.50</td><td>217.12</td><td>84.40</td><td>34.12</td><td>21.73</td><td>0.55</td></tr><tr><td>1800s</td><td>279.75</td><td>115.99</td><td>18.54</td><td>422.49</td><td>170.63</td><td>56.62</td><td>38.50</td><td>1.37</td></tr><tr><td>1850s</td><td>434.05</td><td>192.64</td><td>28.28</td><td>616.27</td><td>297.43</td><td>71.66</td><td>53.08</td><td>1.93</td></tr><tr><td>1860s</td><td>492.79</td><td>217.85</td><td>31.99</td><td>713.12</td><td>293.05</td><td>83.36</td><td>58.34</td><td>1.94</td></tr></tbody></table>

Source: Broadberry et al. (2015)
Total arable, livestock, and agricultural output (log scale, 1700=100)

<figure><img src="image_37.png" /></figure>

Source: Broadberry et al. (2015)
* Total value of net agricultural output is the product of volumes and respective prices.

* First, need to construct a (constant) price series of each output.

* Second, compute the share of each output in total value.

* Third, shares are used as weights in the construction of the agricultural real output index.

* Fourth, divide by population to get per capita output.
* Total value of net agricultural output is the product of volumes and respective prices.

* First, need to construct a (constant) price series of each output.

* Second, compute the share of each output in total value.

* Third, shares are used as weights in the construction of the agricultural real output index.

* Fourth, divide by population to get per capita output.

* See later for predictions on data scarce periods.
<figure><img src="image_38.png" /><figcaption>Sectoral price indices (1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
Current and constant-price shares of outputs in agriculture

<table><thead><tr><th rowspan="2">Decade</th><th colspan="2">Current prices</th><th colspan="2">Constant 1700 prices</th><th rowspan="2">Total agricultural output (1700 = 100)</th></tr><tr><th>Arable (%)</th><th>Livestock (%)</th><th>Arable (%)</th><th>Livestock (%)</th></tr></thead><tbody><tr><td>1270s</td><td>60.1</td><td>39.9</td><td>69.2</td><td>30.8</td><td>62</td></tr><tr><td>1300s</td><td>51.2</td><td>48.8</td><td>66.4</td><td>33.6</td><td>67</td></tr><tr><td>1350s</td><td>48.8</td><td>51.2</td><td>53.3</td><td>46.7</td><td>51</td></tr><tr><td>1400s</td><td>46.3</td><td>53.7</td><td>57.5</td><td>42.5</td><td>45</td></tr><tr><td>1450s</td><td>38.4</td><td>61.6</td><td>53.1</td><td>46.9</td><td>47</td></tr><tr><td>1500s</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>52</td></tr><tr><td>1550s</td><td>58.1</td><td>41.9</td><td>60.5</td><td>39.5</td><td>56</td></tr><tr><td>1600s</td><td>58.1</td><td>41.9</td><td>58.8</td><td>41.2</td><td>83</td></tr><tr><td>1650s</td><td>64.5</td><td>35.5</td><td>64.0</td><td>36.0</td><td>103</td></tr><tr><td>1700s</td><td>59.7</td><td>40.3</td><td>61.5</td><td>38.5</td><td>112</td></tr><tr><td>1750s</td><td>57.8</td><td>42.2</td><td>54.6</td><td>45.4</td><td>138</td></tr><tr><td>1800s</td><td>48.5</td><td>51.5</td><td>45.3</td><td>54.7</td><td>225</td></tr><tr><td>1850s</td><td>44.8</td><td>55.2</td><td>44.2</td><td>55.8</td><td>341</td></tr><tr><td>1860s</td><td>40.0</td><td>60.0</td><td>44.3</td><td>55.7</td><td>375</td></tr></tbody></table>
<figure><img src="image_39.png" /><figcaption>Population of England</figcaption></figure>

Source: Our World in Data, Bank of England (2014)
<figure><img src="image_40.png" /><figcaption>Agricultural output per head (log scale, 1300=100)</figcaption></figure>

Source: Broadberry et al. (2015)
* Methodology similar to agricultural output:

* Output in volumes for each sector.

* Construct (constant) price series for each sector.

* Compute share of each output in total value.

* Use shares as weights in the construction of output index.

* Divide by population.
* Methodology similar to agricultural output:

* Output in volumes for each sector.

* Construct (constant) price series for each sector.

* Compute share of each output in total value.

* Use shares as weights in the construction of output index.

* Divide by population.

* Additional issue: value added and double-counting.

* Project back value added weights in specific benchmark year.

* When possible, distinguish between degree of processing (pig iron vs bar iron).
# Britain's industrial output

* Many databases for the main industrial sectors up to 1700:

* Metals and mining:

* Iron: capacity of blast furnaces and periods of activity. King (2005)

* Tin: receipts of coinage dues. Hatcher (1973)

* Textiles and leather:

* Wool and woolen cloth: detailed export records. Carus-Wilson and Coleman (1963)

* Leather and food processing: reconstruction of agricultural sector. Broadberry et al. (2015)

* Other sectors:

* Food processing: reconstruction of agricultural sector. Broadberry et al. (2015)

* Construction: cathedral building, house building (urban population).

* Book production: titles listed by British Library.
<figure><img src="image_41.png" /><figcaption>Output of metals and mining industries (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_42.png" /><figcaption>Output of textile and leather industries (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_43.png" /><figcaption>Output of other industries (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_44.png" /><figcaption>Industrial output by major sub-sectors (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_45.png" /><figcaption>Industrial output (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
* Method from Deane and Cole (1962).

* Broken down into subsectors:

* Commerce: trade, freight transport, financial services.

* Housing and domestic services: rate of population growth.

* Government: revenues. O'Brien and Hunt (1999)
# Britain's services sector output

<figure><img src="image_46.png" /><figcaption>Service-sector output (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
* Putting things together:

* Combine output series with sectoral weights based on input-output tables for 1841 to adjust for double accounting. Horrell et al. (1994)

* Use price series to convert into nominal series. Clark (2004, 2005, 2006) Beveridge (1939)

* Create a chained index of GDP.

* Divide by population to estimate GDP per capita.
* Putting things together:

* Combine output series with sectoral weights based on input-output tables for 1841 to adjust for double accounting. Horrell et al. (1994)

* Use price series to convert into nominal series. Clark (2004, 2005, 2006) Beveridge (1939)

* Create a chained index of GDP.

* Divide by population to estimate GDP per capita.

* Attempts from income side, but changes in working hours ("industrious revolution" 1700–1820).
<figure><img src="image_47.png" /><figcaption>Sectoral price indices (1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_48.png" /><figcaption>Inter-sectoral terms of trade (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
## Sectoral shares in nominal GDP

<table><thead><tr><th>Year</th><th>Territory</th><th>Agriculture (%)</th><th>Industry (%)</th><th>Services (%)</th><th>Total (%)</th></tr></thead><tbody><tr><td>1381</td><td>England</td><td>45.5</td><td>28.8</td><td>25.7</td><td>100.0</td></tr><tr><td>1522</td><td>England</td><td>39.7</td><td>38.7</td><td>21.6</td><td>100.0</td></tr><tr><td>1600</td><td>England</td><td>41.1</td><td>36.2</td><td>22.7</td><td>100.0</td></tr><tr><td>1700</td><td>England and Britain</td><td>26.7</td><td>41.3</td><td>32.0</td><td>100.0</td></tr><tr><td>1759</td><td>Britain</td><td>29.7</td><td>35.2</td><td>35.1</td><td>100.0</td></tr><tr><td>1801</td><td>Britain</td><td>31.3</td><td>32.7</td><td>36.0</td><td>100.0</td></tr><tr><td>1841</td><td>Britain</td><td>22.1</td><td>36.4</td><td>41.5</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015)
<figure><img src="image_49.png" /><figcaption>Real GDP (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_50.png" /><figcaption>Real GDP, population, and real GDP per capita (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
<figure><img src="image_51.png" /><figcaption>Real GDP per capita (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
* Rely on modelling and proxies.
* Rely on modelling and proxies.

* Instead of measuring agricultural output, derive it from demand for food. Allen (2000)

$$Q^A = rcN$$

* $Q^A$: real agricultural output.

* $r$: ratio of production to consumption (often $r = 1$).

* $c$: per capita consumption.

* $N$: population.
* Real consumption per capita assumed to be a function of its own real price ($P^A/P$), real prices of other goods ($P^{NA}/P$) and real income per capita ($y$).
* Real consumption per capita assumed to be a function of its own real price ($P^A/P$), real prices of other goods ($P^{NA}/P$) and real income per capita ($y$).

* In log-linear form:

$$ \ln c = \alpha_0 + \alpha_1 \ln (P^A/P) + \alpha_2 \ln (P^{NA}/P) + \beta \ln y $$

* $P^A/P$: agricultural product prices in real terms.

* $P^{NA}/P$: non-agricultural product prices in real terms.

* $y$: real income per capita.

* $\alpha_0$: constant.

* $\alpha_1$: own-price elasticity of demand.

* $\alpha_2$: cross-price elasticity of demand.

* $\beta$: income elasticity of demand.
* Adding-up property of linear demand:

$$ \alpha_1 + \alpha_2 + \beta = 0 $$
* Adding-up property of linear demand:

$$ \alpha_1 + \alpha_2 + \beta = 0 $$

* Values from developing countries:

* $\alpha_1 = -0.6$.

* $\alpha_2 = 0.1$.

* $\Rightarrow \beta = 0.5$.

* With price and wage series, get agricultural output from income side.
* Estimate non-agricultural output $Q^{NA}$:

$$Q = Q^A + Q^{NA} \iff Q = \frac{Q^A}{1 - (Q^{NA}/Q)}$$

* $Q^{NA}/Q$: share non-agricultural output.

⇒ Proxied by urbanization rate, e.g., Bairoch (1988).
* Many use long-run estimate inappropriately.

* Careful to distinguish direct and indirect approaches.

* Same series can mix both e.g. Netherlands pre-1510 vs 1510-1807. van Zanden and van Leeuwen (2012)
Data reliability grades

<table><thead><tr><th colspan="3">A. Data reliability grades</th></tr><tr><th>Reliability grade</th><th>Margin of error</th><th>Average<br>margin of error</th></tr></thead><tbody><tr><td>A. Firm figures</td><td>± less than 5%</td><td>± 2.5%</td></tr><tr><td>B. Good figures</td><td>± 5% to 15%</td><td>± 10%</td></tr><tr><td>C. Rough estimates</td><td>± 15% to 25%</td><td>± 20%</td></tr><tr><td>D. Conjectures</td><td>± more than 25%</td><td>± 40%</td></tr></tbody></table>

Source: Broadberry (2021) based on Feinstein (1972)
B. Reliability assessments for GDP in the Netherlands, China, and Japan

<table><thead><tr><th></th><th>Grade</th></tr></thead><tbody><tr><td colspan="2"><strong>Netherlands</strong></td></tr><tr><td>1347-1510</td><td>C</td></tr><tr><td>1510-1650</td><td>B</td></tr><tr><td>1650-1750</td><td>A</td></tr><tr><td>1750-1807</td><td>B</td></tr><tr><td colspan="2"><strong>China</strong></td></tr><tr><td>Northern Song (980-1120)</td><td>B</td></tr><tr><td>Ming (1400-1620)</td><td>B</td></tr><tr><td>Qing (1690-1840)</td><td>A</td></tr><tr><td colspan="2"><strong>Japan</strong></td></tr><tr><td>Ancient (730-1150)</td><td>D</td></tr><tr><td>Medieval (1250-1450)</td><td>C</td></tr><tr><td>Tokugawa (1600-1846)</td><td>B</td></tr></tbody></table>

Source: Broadberry (2021) based on van Zanden and van Leeuwen (2012) and Broadberry, Guan and Li (2018)
* Comparisons over time:

* Inflation rates make output value change.

* Convert all values into 1990 monetary units.
* Comparisons over time:

* Inflation rates make output value change.

* Convert all values into 1990 monetary units.

* Comparisons over space:

* Nominal exchange rates fluctuate independently of local prices.

* Purchasing power parity (PPP) between two currencies: ratio of prices of same basket of goods.

* Accounts for price and expenditure weights differences across countries

⇒ International weights: 1990 Geary-Khamis international dollars
* Comparisons over time:

* Inflation rates make output value change.

* Convert all values into 1990 monetary units.

* Comparisons over space:

* Nominal exchange rates fluctuate independently of local prices.

* Purchasing power parity (PPP) between two currencies: ratio of prices of same basket of goods.

* Accounts for price and expenditure weights differences across countries

⇒ International weights: 1990 Geary-Khamis international dollars

* Benchmark: 1990 GKI$ 400 ≃ subsistence level.
* Within countries:

* Deflation issues: changing baskets of goods over time.

* Heterogeneous sources: systematic and changing biases.
* Within countries:

* Deflation issues: changing baskets of goods over time.

* Heterogeneous sources: systematic and changing biases.

* Across countries:

* PPP conversions: incomparable consumption basket of goods e.g. India vs GB.

* Heterogeneous sources again.

⇒ Current issues with measurement relevant for HGDP estimates.
* Within countries:

* Deflation issues: changing baskets of goods over time.

* Heterogeneous sources: systematic and changing biases.

* Across countries:

* PPP conversions: incomparable consumption basket of goods e.g. India vs GB.

* Heterogeneous sources again.

⇒ Current issues with measurement relevant for HGDP estimates.

* Main point of HGDP is not precision but order of magnitude.

* Still: need reliable comparative picture of living standards.

⇒ Don't take Maddison at face value, check underlying sources.
# The Great Divergence

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_52.png" /></figure>

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

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

Toulouse
School of
Economics
When did countries first start to diverge?
Broadberry (2021) Goldstone (2021)

* Europe's Little Divergence

* Asia's Little Divergence

* The Great Divergence

* Reconsidering the Great Divergence
* First global historical estimates by Maddison (2001) *The World Economy: A Millennial Perspective*.

* Traditional view: gap in living standards between Europe and Asia starting in 1500, perhaps 1300.

* Revising traditional view: Pomeranz (2000) argues gap emerged after 1800.

* Latest estimates: Great Divergence around 1700.

* Careful re-examination of recent data sources: probably later, in the late 1700s.
<figure><img src="image_55.png" /><figcaption>GDP per Capita in Europe, 1270-1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Maddison (2010).
<figure><img src="image_56.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
<figure><img src="image_57.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
## GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)

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

Source: Broadberry (2020) based on Palma and Reis (2019), Ridolfi (2016), Krantz (2017), Schon and Krantz (2012), Malinowski and van Zanden (2017).
* First turning point: Black Death (1348).

* Before: Italy and Spain richer than GB and NL.

* After: GB and NL permanently faster (and richer than pre-Black Death).
* First turning point: Black Death (1348).

* Before: Italy and Spain richer than GB and NL.

* After: GB and NL permanently faster (and richer than pre-Black Death).

* Second turning point: trade with Asia and Americas (1500).

* NL catches Italy in 16th century.

* GB catches Italy in 17th century.
* First turning point: Black Death (1348).

* Before: Italy and Spain richer than GB and NL.

* After: GB and NL permanently faster (and richer than pre-Black Death).

* Second turning point: trade with Asia and Americas (1500).

* NL catches Italy in 16th century.

* GB catches Italy in 17th century.

* No trend in growth in other European countries.
* First turning point: Black Death (1348).

* Before: Italy and Spain richer than GB and NL.

* After: GB and NL permanently faster (and richer than pre-Black Death).

* Second turning point: trade with Asia and Americas (1500).

* NL catches Italy in 16th century.

* GB catches Italy in 17th century.

* No trend in growth in other European countries.

⇒ Europe's Little Divergence (North-West vs rest) circa 1500
<figure><img src="image_59.png" /><figcaption>GDP per Capita in Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Maddison (2010).
<figure><img src="image_60.png" /><figcaption>GDP per Capita in Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015), Broadberry, Custodis and Gupta (2015)
* China:

* Leadership in Asia until 18th century.

* Decline in per capita income 18th-early 20th century.
## China:

* Leadership in Asia until 18th century.

* Decline in per capita income 18th-early 20th century.

## Japan:

* Progress before Meiji (1868) during 17th century.

* Overtake China one century before Meiji.
* China:

* Leadership in Asia until 18th century.

* Decline in per capita income 18th-early 20th century.

* Japan:

* Progress before Meiji (1868) during 17th century.

* Overtake China one century before Meiji.

* India: stagnation and declining trend since 15th century.
* China:

* Leadership in Asia until 18th century.

* Decline in per capita income 18th-early 20th century.

* Japan:

* Progress before Meiji (1868) during 17th century.

* Overtake China one century before Meiji.

* India: stagnation and declining trend since 15th century.

⇒ Asia's Little Divergence (Japan vs China) circa 1700
<figure><img src="image_61.png" /><figcaption>GDP per Capita in Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Maddison (2010).
# The Great Divergence

<figure><img src="image_62.png" /><figcaption>GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015).
# The Great Divergence

<figure><img src="image_63.png" /><figcaption>GDP per Capita in Leading Regions of China and Europe, 980–1850 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) based on Broadberry, Guan and Li (2018).
* Year 1000:

* Leadership of China at $1,000.

* Italy ≈ $ 911; Britain ≈ $ 723.
# The Great Divergence

* Year 1000:

* Leadership of China at $1,000.

* Italy ≈ $ 911; Britain ≈ $ 723.

* Did Great Divergence start 13–14th?
* Year 1000:

* Leadership of China at $1,000.

* Italy ≈ $ 911; Britain ≈ $ 723.

* Did Great Divergence start 13–14th?

* China much larger than any European country (160M vs 19M France in 1600).

* Compare leading regions (Yangzi Delta vs Italy, NL, UK)
# The Great Divergence

* Year 1000:

* Leadership of China at $1,000.

* Italy ≈ $ 911; Britain ≈ $ 723.

* Did Great Divergence start 13–14th?

* China much larger than any European country (160M vs 19M France in 1600).

* Compare leading regions (Yangzi Delta vs Italy, NL, UK)

⇒ The Great Divergence (Europe vs Asia) circa 1700
## Goldstone (2021)

* Modern economic growth is sustained growth in income per capita *together with* population growth.

* Might be later than 1700, probably late 1700s:

* Holland: no gain in per capita income for 200 years after 1600.

* Britain: no sustained simultaneous growth of per capita income and population during same half-century prior to 1750.

* Global comparisons: gains in Britain and Holland in 1750–1800 not unprecedented.
* The Malthusian trap was broken by Schumpeterian growth several times before industrialization, though for only a few decades.

* In Europe: Renaissance Italy or Golden Age Holland.

* Holland's Golden Age in 15-16th century:

* Cluster of innovations in ship design, construction, commercial farming, use of wind power and hydraulics.

* Collapse of Antwerp mid-16th: dominance of Holland over Baltic and north Atlantic trade.

* But surging population halted gains early 17th, leading to long stagnation.
<figure><img src="image_64.png" /><figcaption>GDP and GDP per capital in Holland (1510–1800, constant 1800 guilders)</figcaption></figure>

Source: Goldstone (2021).
Growth in Holland (1510s–1800s, 1500 levels = 100)

<table><thead><tr><th></th><th>GDP</th><th>GDP growth since prior date</th><th>Pop</th><th>Population growth since prior date</th><th>GDP/cap</th><th>GDP/cap growth since prior date</th></tr></thead><tbody><tr><td>1510s</td><td>100</td><td></td><td>100</td><td></td><td>100</td><td></td></tr><tr><td>1540s</td><td>111.1</td><td>.35</td><td>121.4</td><td>.64%</td><td>91.4</td><td>-.30%</td></tr><tr><td>1590s</td><td>241.0</td><td>1.56%</td><td>166.4</td><td>.63%</td><td>144.7</td><td>.92%</td></tr><tr><td>1640s</td><td>389.0</td><td>.96%</td><td>271.4</td><td>.98%</td><td>143.3</td><td>-.02%</td></tr><tr><td>1700s</td><td>418.5</td><td>.12%</td><td>301.6</td><td>.18%</td><td>138.8</td><td>-.05%</td></tr><tr><td>1800s</td><td>407.9</td><td>-.03%</td><td>269.9</td><td>-.11%</td><td>151.2</td><td>.08%</td></tr></tbody></table>

Source: Goldstone (2021).
* 1540s–1560s: strong growth until Dutch war for Independence with Spain and structural change (stable hereafter).

* Share of agriculture: 22%→13%→11% (1750s).

* Share of industry: 30%→22%→30% (1750s).

* Share of services: 48%→65%→59% (1750s).

* Stagnation after the 1640s:

* 1540–1640: growth of GDP by 300%.

* 1640–1750: growth of GDP by 5%.

* 1750–1790: growth of GDP by 10%.
* The break of Malthusian trap was short-lived.

* 1540s–1590s: GDP per capital growth of 56% (0.9% per year), with population growth of 37%.

* 1590s–1670s: population growth > GDP growth.

* 1670s–1800s: population decline.

* GDP per capita of 1590s not reached until 1760s.

* Holland did not achieve modern economic growth until after 1800s.
* Clear break with Malthusian patterns by 1700 or 1800, after industrialization?

## Growth in Britain (1270s–1860s)

<table><thead><tr><th></th><th>Population</th><th>Real GDP per head</th></tr></thead><tbody><tr><td><strong>England</strong></td><td></td><td></td></tr><tr><td>1270s-1300s</td><td>.23</td><td>-.02</td></tr><tr><td>1300s-1340s</td><td>-.06</td><td>.07</td></tr><tr><td>1340s-1400s</td><td>-1.32</td><td>.57</td></tr><tr><td>1400s-1450s</td><td>-.14</td><td>-.07</td></tr><tr><td>1450s-1500s</td><td>.29</td><td>.11</td></tr><tr><td>1500s-1550s</td><td>.66</td><td>-.16</td></tr><tr><td>1550s-1600s</td><td>.64</td><td>.18</td></tr><tr><td>1600s-1650s</td><td>.45</td><td>-.05</td></tr><tr><td>1650s-1700s</td><td>-.06</td><td>.79</td></tr><tr><td><strong>Great Britain</strong></td><td></td><td></td></tr><tr><td>1700s-1750s</td><td>.31</td><td>.18</td></tr><tr><td>1750s-1800s</td><td>.82</td><td>.39</td></tr><tr><td>1800s-1830s</td><td>1.43</td><td>.41</td></tr><tr><td>1830s-1860s</td><td>1.17</td><td>1.15</td></tr></tbody></table>

Source: Goldstone (2021).
* Only two periods see rapid gains in GDP per capita in 1270s–1750s.

* 1340s–1400s: 0.57% per year.

* 1650s–1700s: 0.79% per year.

* Average for other periods pre-1750s: 0.0003% per year (12% overall).
* Only two periods see rapid gains in GDP per capita in 1270s–1750s.

* 1340s–1400s: 0.57% per year.

* 1650s–1700s: 0.79% per year.

* Average for other periods pre-1750s: 0.0003% per year (12% overall).

* Two episodes due to population decline:

* 1340s–1400s: Black Death (reaching Spain of 1250s, far behind Italy).

* 1650s–1700s: also slight population decline (−0.6% per year), with little structural change.
* Is 1700s-1750s modern economic growth?

* GDP per capita growth of 0.18% per year.

* Weak population growth (lower than any half-century of growth since 1450s).

* Similar patterns as those before, e.g., 1550s.

* Onset in modern economic growth no earlier than 1750s.
* Break from prior trends after 1750? According to Broadberry et al. (2015), yes: strong population and GDP per capita growth.

* But doubts about validity of estimates for agricultural output of Broadberry et al. (2015) for 1700–1800.

* Broadberry et al. (2015) claim GDP per capita growth in agriculture of 0.55% per year 1700s–1750s and 0.93% per year 1750s–1800s.

* Using a demand-side approach, Nuvolari and Ricci (2013) find decline in 1750s–1800s. Allen (2000) also.

* Clark (2018) finds similar results (overall decline of 35% rather than growth of 5%).
Estimates for agricultural output in Britain (1500s–1800s)

<table><thead><tr><th></th><th>Allen</th><th>Nuvolari/Ricci</th><th>Clark</th><th>Broadberry et al.</th></tr></thead><tbody><tr><td>1500/22</td><td>131.2</td><td>126.4</td><td>140.3</td><td>117.1</td></tr><tr><td>1550s</td><td>102.9</td><td>119.5</td><td>81.3</td><td>97.5</td></tr><tr><td>1600s</td><td>84.3</td><td>93.0</td><td>95.0</td><td>100.9</td></tr><tr><td>1650s</td><td>90.7</td><td>93.2</td><td>91.3</td><td>88.9</td></tr><tr><td>1700s</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>1750s</td><td>110.9</td><td>114.6</td><td>102.6</td><td>117.9</td></tr><tr><td>1800s</td><td><strong>83.5</strong></td><td><strong>86.0</strong></td><td><strong>65.2</strong></td><td><strong>124.4</strong></td></tr></tbody></table>

Source: Goldstone (2021).
* The devil is in the details.

* Difference not in arable output but in livestock products output.

* Broadberry: "Calculating the output of the livestock sector is more speculative than equivalent calculations for the arable sector".

* Broadberry: "Paradoxically, the least well-documented period is that nearest the present".

* Farm animals sparsely documented in farms accounts, which are already sparse.

* Their numbers: 84 lb of meat per year per capita in 1800, compared to 110 in US and 81 in China today. Plausible?
* Trends in wages, consumption, and heights to assess Broadberry's findings.

* Real wages: 0.28% annual gains in 1700s–1750s vs 0.18% in 1750s–1800s (Humphries and Weisdorf 2019).

* Consumption: Decline in kilocalories consumed from 3,100 per day and person in 1700s to 2,800 in 1800s (Wrigley 2010).

* Height: rise until the 1770s but decline to 1800s (Meredith and Oxley 2014).
## Sensitivity of Broadberry et al.'s estimates

<table><thead><tr><th></th><th>Agricultural output</th><th>GDP</th><th>GDP/capita</th></tr></thead><tbody><tr><td>(a) Broadberry et. al.</td><td></td><td></td><td></td></tr><tr><td>1270s-1700s (England)</td><td>.10</td><td>.22</td><td>.18</td></tr><tr><td>1650s-1700s (England)</td><td>.20</td><td>.78</td><td>.79</td></tr><tr><td>1700s-1750s (Britain)</td><td>.55</td><td>.49</td><td>.18</td></tr><tr><td>1750s-1800s (Britain)</td><td>.93</td><td>1.21</td><td>.39</td></tr><tr><td>1700s-1800s (Britain)</td><td>.74</td><td>.85</td><td>.29</td></tr><tr><td>(b) Consensus of other scholars: 25% decline in average output/capita 1750-1800</td><td></td><td></td><td></td></tr><tr><td>1270s-1700s (England)</td><td>.10</td><td>.22</td><td>.18</td></tr><tr><td>1650s-1700s (England)</td><td>.20</td><td>.78</td><td>.79</td></tr><tr><td>1700s-1750s (Britain)</td><td>.55</td><td>.49</td><td>.18</td></tr><tr><td>1750s-1800s (Britain)</td><td>.24</td><td>1.02</td><td>.20</td></tr><tr><td>1700s-1800s (Britain)</td><td>.40</td><td>.75</td><td>.19</td></tr></tbody></table>

Source: Goldstone (2021).

⇒ No break in patterns before 1780 besides 1650-1700 (<0.2% per year)
* Reconsidering the Great Divergence:

* The Great Divergence was sudden and perhaps later than previously thought.

* Identifying exactly when a departure from typical patterns of pre-modern growth in GDP per capita occurred is crucial for explaining that departure.

* Still, "Rather than viewing the changes in the English economy which took place before the mid-18th century as part of a lengthy but unitary process which culminated as the Industrial Revolution, therefore, they should perhaps be regarded differently. Such changes may have been a necessary but were not a sufficient condition for the later transformation which took place" (Shaw-Taylor and Wrigley 2013).
Wages and Living Standards

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

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

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

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

Toulouse
School of
Economics
Beyond historical GDP?
* Methodology

* Global estimates

* Challenges

* Real wages vs GDP per capita
* Alternative ways to measure historical living standards:

* GDP per capita

* Anthropometrics (height)

* Life expectancy

* Real wages
Arroyo and Gary (2023) De Zwart (2023)

* Real wages as a measure of living standards.

* Tells what people earned and could afford at a time and place.

* Wages and prices are relatively easy to collect.

* Allow comparisons between regions and periods.

* Captures short-term variation across time and short-distance variation across space.
Arroyo and Gary (2023) De Zwart (2023)

* Potential answers to many questions abound underlying mechanisms of long-run development.

* Skill-biased wage premia.

* Integration of labor markets.

* Demand for skills following technical and political changes.

* Changes in inequality.
* Data sources for wages and prices in accounting books (Europe and Americas):

* Public institutions (hospitals, churches, charities, municipal governments, the military).

* Private businesses, plantations, manorial estates.

* Households.
* Methodological issues:

* Composition and weights of consumption basket.

* Representativeness of occupation used.

* Number of working days in a year.

* Value of in-kind payments.

* Nature of labor regime (labor market, home production, coercion).
* **Solution:** relate male wages to consumer basket based on caloric needs near subsistence level. Allen (2001)

* Bare-bones basket = necessary nutrients + fuel (heating + lighting).

* Initially: 1940 calories per day. Re-evaluated at 2100 calories per day. Humphries (2013) Allen (2015)

* Income of male laborer relative to cost-of-living of household $\implies$ $3$ (wife + 2 children consuming half basket). Re-evaluated to $\times 4$.

* Re-evaluations pushed down real wage levels, but same trends over time and space.

* Normalize to subsistence level to get a "subsistence ratio".
## BB-baskets for Britain

<table><thead><tr><th>Item</th><th>Unit/Year</th><th>Allen et al., 2011</th><th>Allen, 2015</th></tr></thead><tbody><tr><td>Energy Target per person</td><td>kcal</td><td>1940</td><td>2100</td></tr><tr><td>Main staple (Rye)</td><td>kg</td><td>180</td><td>197</td></tr><tr><td>Beans or peas</td><td>kg</td><td>20</td><td></td></tr><tr><td>Meat</td><td>kg</td><td>5</td><td></td></tr><tr><td>Butter</td><td>kg</td><td>3</td><td></td></tr><tr><td>Linen</td><td>m</td><td>3</td><td></td></tr><tr><td>Lamp oil</td><td>liter</td><td>1.3</td><td></td></tr><tr><td>Soap</td><td>kg</td><td>1.3</td><td></td></tr><tr><td>Candles</td><td>kg</td><td>1.3</td><td></td></tr><tr><td>Fuel</td><td>mBTU</td><td>3</td><td></td></tr><tr><td>Household multiplier</td><td>x</td><td>x 3</td><td>x 4</td></tr><tr><td>Household energy</td><td>kcal</td><td>5820</td><td>8400</td></tr></tbody></table>

Source: De Zwart (2023) based on Allen et al. (2011) and Allen (2015).
* The Little Divergence across Europe

* The Great Divergence between Europe and Asia

* The Americas
## Men's real wages across Europe, computed using subsistence baskets

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

Source: De Zwart (2023) based on Allen (2001), Malanima (2013), Lopez Losa and Piquero (2021), Ridolfi (2019), and Malinowski (2016).
* Using historical GDP estimates, Little Divergence circa 1500 for Netherlands, and 1650 for Britain.

* Using standards of living, seems later: between 1700 and 1720.

* Downward long-run trend, except for Britain.
## Men's real wages across Europe and Asia, computed using subsistence baskets

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

Source: De Zwart (2023) based on Allen et al. (2011), De Zwart and Van Zanden (2015), and Kumon (2022).
* Wages in China were relatively low and declining, as in India.

* Wages in Japan were extremely low, among the poorest in the world.

* Using historical GDP estimates, Great Divergencecirca 1700.

* Using standards of living, seems earlier: perhaps before 1600s.
## Men's real wages across Europe and the Americas, computed using subsistence baskets

<figure><img src="image_70.png" /><figcaption>Source: Arroyo and Gary (2023) based on Allen (2001), Losa and Zarauz (2021), and Geloso (2019).</figcaption></figure>
Men's real wages across Europe and the Americas, computed using subsistence baskets

<figure><img src="image_71.png" /></figure>

Source: Arroyo and Gary (2023) based on Allen (2001), Allen et al. (2012), and Abad et al. (2012).
## Men's real wages across Europe and the Americas, computed using subsistence baskets

<figure><img src="image_72.png" /></figure>

Source: Arroyo and Gary (2023) based on Allen (2001), Allen et al. (2012), and Abad et al. (2012).
* Welfare ratios in Latin America above subsistence levels after colonization, even rising somewhat after the end of forced labor.

* Despite colonization, wages not out of line with much of Europe.

* Exceptionalism in the United States since the 18th century.
* Challenges and issues when using this approach:

* Numerator: wages and labor markets.

* Denominator: prices and baskets.
* Who's wages are compared?

* Are these wages representative for the population at large?

* How do real wages of urban workers compare to rural workers?

* How did systems of labor coercion affect real wages?

* Role of labor contracts and labor organizers for wages?
* Main source of wage for historical comparisons: urban wages of construction workers.

* Wage is main source of income, so indicates standard of living.

* Technology for building stable across time and space.

* Very good records, especially church construction.

* Are these representative?
* Can be representative of broader wages if competitive local labor markets:

* If wages < standard in agriculture, then movement to agriculture ⇒ labor scarcity in urban areas ⇒ wage increases and vice-versa.

* True if free movement from urban to rural areas.

* Many instances of labor market systems of coercion (slavery, corvée).

* Complicated by coexistence of “free” and “unfree” labor in the same area (e.g., Potosi).

* Still, rural and urban wages follow broadly similar long-run patterns, despite some short periods of divergence.
* Often use daily wages to estimate annual real wages.

* Assumptions on the length of the working year.

* Typically 250 work days, but wide variation over space and time (e.g., 150 in Sweden).

* Seasonality compounds these issues.
* Often use daily wages to estimate annual real wages.

* Assumptions on the length of the working year.

* Typically 250 work days, but wide variation over space and time (e.g., 150 in Sweden).

* Seasonality compounds these issues.

* One solution: annual wages based on annual work contracts.

* Compare with daily wages trends if convergence or divergence. Humphries and Weisdorf (2015, 2019)

* Still issue of representativeness of annual contracts in share of all contracts and characteristics of workers (Britain: minority and declining share of contracts + younger unmarried).
* Observed wages paid to laborers or contractors?

* Building contractors commission in early modern London: 20-30%.

* Similar in Paris or Italy, but unclear throughout the world.

* Wages in books of large institutions not representative of regular labor market.

* Cash wages fail to capture payments in kind (lodging, food). Use confidence intervals adding the value of in-kind transfers.
* Focus on male wages, but male breadwinner is not the norm throughout history.

* Should also include women and children's earnings (though substantial fluctuations over time and space).

* Broader picture of family incomes over time, e.g., women and children might contribute more than 50% of total or drive long-run trends.

* Same issues as with male wages, need to account for changes in demographic patterns and labor force participation rates.
## Real wages in England

<figure><img src="image_73.png" /></figure>

Source: De Zwart (2023) based on Horrell et al. (2021).
* Issues in estimating the denominator: the cost of living.

* Laspeyers index.

* Housing costs.

* Global temperature variation.
* Subsistence basket methodology: Laspeyres index.

* Fixed set of goods, fluctuates only with changes in prices.

* But baskets of consumption change over time.

* Establish benchmarks and create chained-Laspeyres index. Horrell (2023)

* Good for comparisons within a country over time but not for comparisons over space, where a bares-bone basket might be more appropriate.
* Housing costs:

* General assumption: before industrialization, housing about 5% of households total expenses per family member.

* Very little information, but generally might be higher.

* Climate adjustments: fuel needs are different across climates.
## Clothing and energy requirements

<table><thead><tr><th></th><th></th><th>Bombay</th><th>St. Petersburg</th><th>St P./Bombay</th></tr></thead><tbody><tr><td>Clothing</td><td>Meter eq.</td><td>23</td><td>63</td><td>3 times more</td></tr><tr><td>Footware</td><td>Meter eq.</td><td>1</td><td>31</td><td>31 times more</td></tr><tr><td>Bedding</td><td>Meter eq.</td><td>3</td><td>21</td><td>7 times more</td></tr><tr><td>Fuel</td><td>mBTU</td><td>3</td><td>25</td><td>8 times more</td></tr><tr><td>Light</td><td>mBTU</td><td>.4</td><td>.9</td><td>2 times more</td></tr></tbody></table>

Source: De Zwart (2023) based on Allen (2017).
## Real wages in Amsterdam and Delhi with adjustments for climate differences

<figure><img src="image_74.png" /></figure>

Source: De Zwart (2023) based on Allen (2007) and Moatsos (2020).
# Real wages vs GDP per capita

## Angeles (2008)

* Why are trends in real wages and GDP per capita so different?
## Angeles (2008)

* Why are trends in real wages and GDP per capita so different?

* Differences can arise with any of three factors:

* Changes in share of national income allocated to labor.

* Changes in labor supply per capita.

* Changes in relative prices.
# Real wages vs GDP per capita

## Real wages in England

<figure><img src="image_75.png" /></figure>

Source: Angeles (2008) based on Allen (2001).
# Real wages vs GDP per capita

<figure><img src="image_76.png" /><figcaption>Real wages in England</figcaption></figure>

Source: Angeles (2008) based on Clark (2005).
# Real wages vs GDP per capita

<figure><img src="image_77.png" /><figcaption>Real wages in England</figcaption></figure>

Source: Angeles (2008) based on Clark (2007).
Real wages vs GDP per capita

<figure><img src="image_78.png" /><figcaption>Real GDP per capita (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
* Construction methodology:

* GDP per capita: output of each sector weighted by sector share, divided by population.

* Real wages: nominal wages deflated with appropriate price index reflecting consumption patterns of typical worker.

* Link between the two: fraction of aggregate output corresponds to total of nominal wages (accounting identity).

* Assume competitive labor market, so that wages across sectors are equalized.
# Real wages vs GDP per capita

* Changes in share of national income allocated to labor.

* Higher share ⇒ growth rate of real wage > growth rate of GDP per capita.

* Highlights changes in distribution of income between labor and other factors of production.
# Real wages vs GDP per capita

* Changes in share of national income allocated to labor.

* Higher share ⇒ growth rate of real wage > growth rate of GDP per capita.

* Highlights changes in distribution of income between labor and other factors of production.

* Changes in labor supply per capita.

* Higher labor supply per capita ⇒ growth rate < growth rate of GDP per capita.

* Highlights changes in supply of labor per capita (c.f. the "industrious revolution" and the rising number of hours worked in 17-18 century England).
# Real wages vs GDP per capita

* Changes in share of national income allocated to labor.

* Higher share ⇒ growth rate of real wage > growth rate of GDP per capita.

* Highlights changes in distribution of income between labor and other factors of production.

* Changes in labor supply per capita.

* Higher labor supply per capita ⇒ growth rate < growth rate of GDP per capita.

* Highlights changes in supply of labor per capita (c.f. the "industrious revolution" and the rising number of hours worked in 17-18 century England).

* Changes in relative prices.

* Ambiguous effects depending on how large and pace of growth of sector with appreciation.
Real wages vs GDP per capita

<figure><img src="image_79.png" /><figcaption>Real wage and GDP per capita (1700=100)</figcaption></figure>

Source: Angeles (2008) based on Craft and Harley (1992), Wrigley et al. (1997), Allen (2001), and Clark (2005, 2007)
* Application to 18th century England

* All wage series follow similar patterns: growth in 1700–50, decline in 1750–60, stagnation in 1760–1800.

* Most series show real wages in 1800 are 5% higher than in 1700.

* Relative to GDP, no divergence until 1750–60.

* In these data, GDP growth of 37% over the century.
Real wages vs GDP per capita

* Data needed to test the theory:

* Population: every 5 years. Wrigley et al. (1997)

* Changes in income distribution: share of labor vs non-labor income based on share of income in top 10%. Lindert and Williamson (1983).

* Labor supply per capita: hours worked per year from court proceedings. Voth (2000, 2001)

* Price information on all sectors: only 7 agriculture and 5 industry. Clark (2005)
# Real wages vs GDP per capita

## Data sources

<table><thead><tr><th colspan="4">(a) Crafts (1983) and Crafts and Harley (1992)</th></tr><tr><th colspan="4">Annual growth rate of output per sector, in percentage</th></tr><tr><th></th><th>1700-1760</th><th>1760-1780</th><th>1780-1800</th></tr></thead><tbody><tr><td>Agriculture</td><td>0.6</td><td>0.13</td><td>0.75</td></tr><tr><td>Industry</td><td>0.71</td><td>1.29</td><td>1.96</td></tr><tr><td>Commerce</td><td>0.69</td><td>0.64</td><td>1.38</td></tr><tr><td>Rent and services</td><td>0.38</td><td>0.69</td><td>0.97</td></tr><tr><td>Government</td><td>1.91</td><td>1.29</td><td>2.11</td></tr><tr><td>Share of sector in GDP</td><td></td><td></td><td></td></tr><tr><td></td><td>1700-1760</td><td>1760-1780</td><td>1780-1800</td></tr><tr><td>Agriculture</td><td>0.37</td><td>0.37</td><td>0.32</td></tr><tr><td>Industry</td><td>0.20</td><td>0.20</td><td>0.25</td></tr><tr><td>Commerce</td><td>0.16</td><td>0.16</td><td>0.16</td></tr><tr><td>Rent and services</td><td>0.20</td><td>0.20</td><td>0.20</td></tr><tr><td>Government</td><td>0.07</td><td>0.07</td><td>0.07</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>(b) Wrigley et al. (1997)</td><td></td><td></td><td></td></tr><tr><td>Population of England, in thousands</td><td>1701</td><td>1761</td><td>1781</td><td>1801</td></tr><tr><td></td><td>5210</td><td>6310</td><td>7206</td><td>8671</td></tr><tr><td>(c) Lindert and Williamson (1983)</td><td></td><td></td><td></td></tr><tr><td>Share of income to the top 10% of the population (calculations with paupers)</td><td>1688</td><td>1759</td><td>1801</td></tr><tr><td>Share of top 10%</td><td>44.0</td><td>44.4</td><td>47.9</td></tr><tr><td>(d) Voth (2001)</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>1760</td><td>1800</td></tr><tr><td>Working hours per year</td><td></td><td>2576</td><td>3328</td></tr><tr><td>Total labour supply</td><td></td><td>100</td><td>172.3</td></tr><tr><td>(e) Clark (2005)</td><td></td><td></td><td></td></tr><tr><td colspan="4">Prices for 7 categories of agricultural goods and 5 categories of industrial goods, available for each decade of the 18th century</td></tr></tbody></table>

Source: Angeles (2008). Assume constant hours worked for 1700 (2,576 hours per year).
# Real wages vs GDP per capita

## Annual working hours per worker (1870–2017)

### Annual working hours per worker

Average working hours per worker over an entire year. Before 1950 the data corresponds only to full-time production workers (non-agricultural activities). Starting in 1950 estimates cover total hours worked in the economy as measured primarily from National Accounts data.

Our World in Data

<figure><img src="image_80.png" /></figure>

Data source: Huberman & Minns (2007) and PWT 9.1 (2019)

OurWorldInData.org/working-hours | CC BY

Note: We plot the data from Huberman & Minns (2007) and extend coverage using an updated vintage of PWT, which uses the same underlying source. Comparisons between countries are limited due to differences in measurement.
# Real wages vs GDP per capita

Explaining the divergence between GDP per capita and real wages

<table><thead><tr><th rowspan="2">Variable</th><th colspan="4">Percentage change over the period (%)</th></tr><tr><th>1700-1760</th><th>1760-1780</th><th>1780-1800</th><th>1700-1800</th></tr></thead><tbody><tr><td>Agriculture</td><td>43.2</td><td>2.6</td><td>16.1</td><td>70.6</td></tr><tr><td>Industry</td><td>52.9</td><td>29.2</td><td>47.4</td><td>191.3</td></tr><tr><td>Commerce</td><td>51.1</td><td>13.6</td><td>31.5</td><td>125.7</td></tr><tr><td>Rent and services</td><td>25.5</td><td>14.7</td><td>21.3</td><td>74.7</td></tr><tr><td>Government</td><td>211.2</td><td>29.2</td><td>51.8</td><td>510.5</td></tr><tr><td>GDP</td><td>54.6</td><td>14.0</td><td>29.9</td><td>129.0</td></tr><tr><td>Population</td><td>21.1</td><td>14.2</td><td>20.3</td><td>66.4</td></tr><tr><td>GDP per capita</td><td>27.7</td><td>-0.2</td><td>8.0</td><td>37.6</td></tr><tr><td>Share of labour (α)</td><td>-0.7</td><td>-3.2</td><td>-3.2</td><td>-7.0</td></tr><tr><td>Labour supply per capita (L/N)</td><td>0</td><td>12.0</td><td>12.0</td><td>25.4</td></tr><tr><td>Real wage</td><td>26.8</td><td>-13.7</td><td>-6.7</td><td>2.1</td></tr></tbody></table>

Source: Angeles (2008).
# Real wages vs GDP per capita

* GDP per capita growth over century: 38%.

* Real wage growth over century: 2%.

* Calculation of growth of real wages if only one factor present:

* If only changes in income distribution, wage growth of 28%.

* If only changes in labor supply per capita, wage growth of 10%.

* If only changes in prices, wage growth of 35%.

⇒ 3/4 of the divergence is explained by increased working hours.
# Patterns of Long-Run Growth in Global Perspective

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

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

Toulouse
School of
Economics
* Britain

* Continental Europe

* Spain

* Italy

* France

* East Asia

* China

* Japan
Britain
<figure><img src="image_84.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
Broadberry et al (2013, 2015, 2017) Broadberry (2021)
Humphries and Weidsorf (2019)

* British economic growth in long-run perspective

* Characteristics of modern economic growth

* Structural change

* Wages and standards of living
# British economic growth in long-run perspective

Real GDP, Population, and Real GDP per Capita in England (1270–1700) and Great Britain (1700–1870), Averages per Decade, Log Scale, 1700 = 100

<figure><img src="image_85.png" /></figure>

Source: Broadberry (2021) based on Broadberry, Guan and Li (2018).
Annual growth rates in (1270–1700) and Great Britain (1700–1870)

<table><thead><tr><th rowspan="2">Period</th><th colspan="5">Annual growth rate (%)</th></tr><tr><th>Population</th><th>Agriculture</th><th>Industry</th><th>Services</th><th>Real GDP</th></tr></thead><tbody><tr><td colspan="6"><strong>A. England</strong></td></tr><tr><td>1270s–1300s</td><td>0.27</td><td>0.24</td><td>0.05</td><td>0.39</td><td>-0.02</td></tr><tr><td>1300s–1350s</td><td>-0.52</td><td>-0.52</td><td>-0.59</td><td>-0.87</td><td>-0.64</td></tr><tr><td>1350s–1400s</td><td>-1.06</td><td>-0.43</td><td>-0.01</td><td>-0.44</td><td>-0.30</td></tr><tr><td>1400s–1450s</td><td>-0.21</td><td>-0.10</td><td>-0.27</td><td>-0.22</td><td>-0.06</td></tr><tr><td>1450s–1500s</td><td>0.25</td><td>0.35</td><td>0.43</td><td>0.45</td><td>0.40</td></tr><tr><td>1500s–1550s</td><td>0.65</td><td>0.31</td><td>0.68</td><td>0.48</td><td>0.51</td></tr><tr><td>1550s–1600s</td><td>0.62</td><td>0.69</td><td>1.00</td><td>0.74</td><td>0.81</td></tr><tr><td>1600s–1650s</td><td>0.51</td><td>0.21</td><td>0.35</td><td>0.90</td><td>0.41</td></tr><tr><td>1650s–1700</td><td>-0.04</td><td>0.20</td><td>1.01</td><td>0.71</td><td>0.78</td></tr><tr><td>1270s–1700</td><td>0.04</td><td>0.10</td><td>0.31</td><td>0.23</td><td>0.22</td></tr><tr><td colspan="6"><strong>B. Great Britain</strong></td></tr><tr><td>1700s–1750s</td><td>0.30</td><td>0.63</td><td>0.56</td><td>0.61</td><td>0.49</td></tr><tr><td>1750s–1800s</td><td>0.71</td><td>0.93</td><td>1.45</td><td>1.32</td><td>1.21</td></tr><tr><td>1800s–1850s</td><td>1.34</td><td>0.78</td><td>2.95</td><td>2.15</td><td>2.08</td></tr><tr><td>1850s–1870</td><td>1.54</td><td>0.99</td><td>3.01</td><td>1.96</td><td>0.12</td></tr><tr><td>1700s–1870</td><td>0.84</td><td>0.79</td><td>1.72</td><td>1.43</td><td>1.31</td></tr></tbody></table>

Source: Broadberry et al. (2015).
* Pre-Back Death (1270–1348): stagnation at low levels despite shocks.

* Harvest crisis of 1316–1318.

* International credit crunch of 1339–40.

* Outbreak of Hundred Years War in 1336.
* Black Death Era (1348–1400s): dramatic demographic shock.

* Black Death ↓ population by 46% in 1348–9.

* Immediate GDP ↓ while GDP per K ↑.

* ↑ in holding sizes, real wages, income.
* Post-Black Death (1400s–1650s): GDP per K plateaus.

* Population shrinks until 1450s to its lowest level (2M).

* Population recovers 1450s–1650s (5.3M) but GDP growth at same rate.

* Still, restructuring from agriculture to industry.

* One can see GDP per capita as resilient to population growth.
* Post-Civil War (1650s–1700): a slow “take-off”.

* Population stagnation.

* Strong GDP and GDP per K growth (0.8%).

* GDP per K double in 1690s vs 1290s.

* Still not modern economic growth.
* The “Industrial Revolution” (1700–1850s):

* Population growth resumes.

* GDP per K growth positive.

* Growth positive, stable, no shrinking.

* Relatively slow rates until 19th century.
* The "Industrial Revolution" (1700–1850s):

* Population growth resumes.

* GDP per K growth positive.

* Growth positive, stable, no shrinking.

* Relatively slow rates until 19th century.

* Revision of past views:

* Britain entered the Industrial Revolution richer than assumed.

* Britain grew less during the Industrial Revolution than assumed.
# Characteristics of modern economic growth

Real GDP, Population, and Real GDP per Capita Growth in England (1270–1700) and Great Britain (1700–1870), Averages per 50-Years

<figure><img src="image_86.png" /></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015).
* Pre-industrial growth:

* GDP per K growth fast after Black Death and Civil War.

* Population declines (land/labor $↑ \Rightarrow$ output/capita $↑$)

* Unstable growth with shrinkages.
* Pre-industrial growth:

* GDP per K growth fast after Black Death and Civil War.

* Population declines (land/labor ↑ ⇒ output/capita ↑)

* Unstable growth with shrinkages.

* Modern economic growth:

* GDP per K growth together with population.

* All rates are positive, stable, no shrinkage.
* Pre-industrial growth:

* GDP per K growth fast after Black Death and Civil War.

* Population declines (land/labor ↑ ⇒ output/capita ↑)

* Unstable growth with shrinkages.

* Modern economic growth:

* GDP per K growth together with population.

* All rates are positive, stable, no shrinkage.

* Structural change: shift from agriculture to industry and services.
* Output shares per sector: derived from output approach.

* Labor-force shares per sector: derived from benchmark years.

* Output per worker per sector: ratio of output to labor-force.
* Benchmark years:

* 1381 poll tax returns.

* 1522 muster rolls.

* 1688, 1759, and 1801 social tables.

* 1813–20 parish registers.

* 1851–71 population censuses.
Late-medieval labor-force shares

* Poll tax return of 1381.

* Earliest document to estimate occupational structure of England.

* 30k individuals (2% of adults) across 892 towns.

* Attribute the 15% of "laborers" to each sector in proportion of known occupations.

* Weighting procedure for national representativeness of rural (80%), urban (10%), semi-rural populations (10%).

* Weight women market participation 30% relative to men (based on census 1851).
Late-medieval labor-force shares

Sectoral distribution of English labor-force in 1381

<table><thead><tr><th rowspan="2">Sector</th><th colspan="2">Weighted number of workers with known occupations</th><th colspan="2">Allocated labourers with no stated occupations</th><th colspan="3">Total workers</th></tr><tr><th>Males (no.)</th><th>Females (no.)</th><th>Males (no.)</th><th>Females (no.)</th><th>Males (no.)</th><th>Females (no.)</th><th>Males and females (no.)</th></tr></thead><tbody><tr><td>Agriculture</td><td>14,351</td><td>1,467</td><td>2,526</td><td>288</td><td>16,877</td><td>1,755</td><td></td></tr><tr><td>Industry</td><td>2,602</td><td>899</td><td>1,244</td><td>547</td><td>3,846</td><td>1,446</td><td></td></tr><tr><td>Services</td><td>4,480</td><td>1,888</td><td>0</td><td>0</td><td>4,480</td><td>1,888</td><td></td></tr><tr><td>Total</td><td>21,433</td><td>4,254</td><td>3,770</td><td>835</td><td>25,203</td><td>5,089</td><td></td></tr><tr><td></td><td>%</td><td>%</td><td>%</td><td>%</td><td>%</td><td>%</td><td>%</td></tr><tr><td>Agriculture</td><td>67.0</td><td>34.5</td><td>67.0</td><td>34.5</td><td>67.0</td><td>34.5</td><td>57.2</td></tr><tr><td>Industry</td><td>12.1</td><td>21.1</td><td>33.0</td><td>65.5</td><td>15.2</td><td>28.4</td><td>19.2</td></tr><tr><td>Services</td><td>20.9</td><td>44.4</td><td>0.0</td><td>0.0</td><td>17.8</td><td>37.1</td><td>23.6</td></tr><tr><td>Total</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015).
Late-medieval labor-force shares

* Muster rolls or military surveys of 1522.

* Only 3 counties with occupations (one rural, one urban, one semi-rural).

* Only men.

* Attribute the 26% of "laborers" to each sector in proportion of known occupations.

* Weighting procedure for national representativeness of rural (80%), urban (10%), semi-rural populations (10%).

* Assume women participate 30% relative to men and across sectors as in 1381.
Late-medieval labor-force shares

<table><caption>Sectoral distribution of English labor-force in 1522</caption><thead><tr><th>Sector</th><th>Coventry (Urban) (10%)</th><th>Babergh (Semi-rural) (10%)</th><th>Rutland (rural) (80%)</th><th colspan="2">Weighted number of male workers with known occupations</th></tr></thead><tbody><tr><td>A.</td><td>No.</td><td>No.</td><td>No.</td><td>No</td><td>%</td></tr><tr><td>Agriculture</td><td>12</td><td>273</td><td>868</td><td>1,848</td><td>64.7</td></tr><tr><td>Industry</td><td>594</td><td>577</td><td>38</td><td>471</td><td>16.5</td></tr><tr><td>Services</td><td>143</td><td>133</td><td>218</td><td>537</td><td>18.8</td></tr><tr><td>TOTAL</td><td>749</td><td>983</td><td>1,124</td><td>2,856</td><td>100.0</td></tr><tr><td></td><td colspan="2">Weighted number of male workers with known occupations (from A)</td><td colspan="2">Allocated male labourers with no stated occupations:</td><td>Total male workers</td><td>Total female workers</td><td>Weighted total workers</td></tr><tr><td>B.</td><td>No.</td><td>%</td><td>No.</td><td>%</td><td>%</td><td>%</td><td>%</td></tr><tr><td>Agriculture</td><td>1,848</td><td>64.7</td><td>644</td><td>64.7</td><td>2,492</td><td>64.7</td><td>34.5</td></tr><tr><td>Industry</td><td>471</td><td>16.5</td><td>352</td><td>35.3</td><td>823</td><td>21.4</td><td>28.4</td></tr><tr><td>Services</td><td>537</td><td>18.8</td><td>0</td><td>0</td><td>537</td><td>13.9</td><td>37.1</td></tr><tr><td>TOTAL</td><td>2,856</td><td>100.0</td><td>996</td><td>100.0</td><td>3,852</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015).
* Use social tables by contemporaries analyzed by Lindert and Williamson (1982).

* 1688 by Gregory King (1696).

* 1759 by Joseph Massie (1760).

* 1801 by Patrick Colquhoun (1806).

* Produced to estimate tax base in wartime across “social classes”.

* Based on parish registers and specific tax revenues, improved with various local censuses.
Labor-force shares in social tables 1688–1801

Sectoral distribution of English labor-force in 1688

<table><thead><tr><th rowspan="2">Sectors</th><th rowspan="2">Occupations</th><th colspan="3">'Family' numbers (i.e. males)</th><th colspan="4">Labour-force shares<br>(2, 3 and 4 as % of 7)</th></tr><tr><th>According to<br>King<sup>a</sup></th><th>Adjusted for labourers<br>and servants<sup>a</sup></th><th>Occupied</th><th>Males</th><th>Females<sup>c</sup></th><th>Total<sup>f</sup></th></tr></thead><tbody><tr><td>1. Unoccupied</td><td>High titles and<br>gentlemen</td><td>19,626</td><td>43,115</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Vagrants</td><td>23,489</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2. Agriculture</td><td>Agriculture</td><td>227,440</td><td>227,440</td><td>635,399</td><td>46.0</td><td>22.3</td><td>38.9</td></tr><tr><td></td><td>Agricultural<br>labourers<sup>b</sup></td><td>Included in 5</td><td>407,959</td><td></td><td></td><td></td><td></td></tr><tr><td>3. Industry</td><td>Industry and<br>building</td><td>256,866</td><td>256,866</td><td>447,087</td><td>32.3</td><td>37.8</td><td>34.0</td></tr><tr><td></td><td>Industrial labourers<sup>c</sup></td><td>Included in 5</td><td>190,221</td><td></td><td></td><td></td><td></td></tr><tr><td>4. Services</td><td>Commerce</td><td>128,025</td><td>128,025</td><td>299,750</td><td>21.7</td><td>39.9</td><td>27.2</td></tr><tr><td></td><td>Professions</td><td>42,960</td><td>42,960</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Military and<br>maritime</td><td>94,000</td><td>94,000</td><td></td><td></td><td></td><td></td></tr><tr><td>5. Labourers, Cottagers and Paupers</td><td></td><td>598,180</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6. TOTAL (1–5)</td><td></td><td>1,390,586</td><td>1,425,351</td><td></td><td></td><td></td><td></td></tr><tr><td>7. TOTAL OCCUPIED (2–4)</td><td></td><td></td><td></td><td>1,382,236</td><td>100.0</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015).
Labor-force shares in social tables 1688–1801

Sectoral distribution of English labor-force in 1759

<table><thead><tr><th rowspan="2">Sectors</th><th rowspan="2">Occupations</th><th colspan="2">'Family' numbers (i.e. males)</th><th rowspan="2">Occupied</th><th colspan="3">Labour-force shares<br>(2, 3 and 4 as % of 7)</th></tr><tr><th>According to Massie<sup>d</sup></th><th>Adjusted for labourers and servants<sup>d</sup></th><th>Males</th><th>Females<sup>e</sup></th><th>Total<sup>f</sup></th></tr></thead><tbody><tr><td>1. Unoccupied</td><td>High titles and gentlemen</td><td>18,070</td><td>31,488</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Vagrants</td><td>13,418</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2. Agriculture</td><td>Agriculture</td><td>379,008</td><td>379,008</td><td>664,692</td><td>43.0</td><td>22.3</td><td>36.8</td></tr><tr><td></td><td>Agricultural labourers<sup>b</sup></td><td>Included in 5</td><td>285,684</td><td></td><td></td><td></td><td></td></tr><tr><td>3. Industry</td><td>Industry and building</td><td>366,252</td><td>366,252</td><td>499,460</td><td>32.3</td><td>37.8</td><td>33.9</td></tr><tr><td></td><td>Industrial labourers<sup>e</sup></td><td>Included in 5</td><td>133,208</td><td></td><td></td><td></td><td></td></tr><tr><td>4. Services</td><td>Commerce</td><td>200,500</td><td>200,500</td><td>381,979</td><td>24.7</td><td>39.9</td><td>29.3</td></tr><tr><td></td><td>Professions</td><td>57,000</td><td>57,000</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Military and maritime</td><td>86,000</td><td>86,000</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Domestic servants<sup>d</sup></td><td>Omitted</td><td>38,479</td><td></td><td></td><td></td><td></td></tr><tr><td>5. Labourers, cottagers and paupers</td><td></td><td>418,892</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6. TOTAL (1–5)</td><td></td><td>1,539,140</td><td>1,577,619</td><td></td><td></td><td></td><td></td></tr><tr><td>7. TOTAL OCCUPIED (2–4)</td><td></td><td></td><td></td><td>1,546,131</td><td>100.0</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015).
Labor-force shares in social tables 1688–1801

Sectoral distribution of English labor-force in 1801

<table><thead><tr><th rowspan="2">Sectors</th><th rowspan="2">Occupations</th><th colspan="2">'Family' numbers (i.e. males)</th><th colspan="4">Labour-force shares<br>(2, 3 and 4 as % of 7)</th></tr><tr><th>According to<br>Colquhoun<sup>d</sup></th><th>Adjusted for labourers<br>and servants<sup>d</sup></th><th>Occupied</th><th>Males</th><th>Females<sup>e</sup></th><th>Total<sup>f</sup></th></tr></thead><tbody><tr><td>1. Unoccupied</td><td>High titles and<br>gentlemen</td><td>27,203</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Vagrants</td><td>179,718</td><td>206,921</td><td></td><td></td><td></td><td></td></tr><tr><td>2. Agriculture</td><td>Agriculture</td><td>320,000</td><td>320,000</td><td>729,322</td><td>35.7</td><td>22.3</td><td>31.7</td></tr><tr><td></td><td>Agricultural<br>labourers<sup>b</sup></td><td>Included in 5</td><td>409,322</td><td></td><td></td><td></td><td></td></tr><tr><td>3. Industry</td><td>Industry and<br>building</td><td>541,026</td><td>541,026</td><td>731,883</td><td>35.9</td><td>37.8</td><td>36.4</td></tr><tr><td></td><td>Industrial<br>labourers<sup>c</sup></td><td>Included in 5</td><td>190,857</td><td></td><td></td><td></td><td></td></tr><tr><td>4. Services</td><td>Commerce</td><td>205,800</td><td>205,800</td><td>579,816</td><td>28.4</td><td>39.9</td><td>31.9</td></tr><tr><td></td><td>Professions</td><td>74,840</td><td>74,840</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Military and<br>maritime</td><td>244,348</td><td>244,348</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Domestic servants<sup>d</sup></td><td>Omitted</td><td>54,828</td><td></td><td></td><td></td><td></td></tr><tr><td>5. Labourers, cottagers and paupers</td><td></td><td>600,179</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6. TOTAL (1–5)</td><td></td><td>2,193,114</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7. TOTAL OCCUPIED (2–4)</td><td></td><td></td><td>2,247,942</td><td></td><td>2,041,021</td><td>100.0</td><td>100.0</td></tr></tbody></table>
* Labor-force shares in 1813–71 from Shaw-Taylor (2009).

• Parish registers (1813–1820).

• Population censuses (1851–1871).
Sectoral distribution of English labor-force in 1813–71

<table><thead><tr><th rowspan="2">Sector</th><th colspan="3">1813-20 (%)</th><th colspan="3">1851 (%)</th><th colspan="3">1861 (%)</th><th colspan="3">1871 (%)</th></tr><tr><th>Males</th><th>Females</th><th>Total</th><th>Males</th><th>Females</th><th>Total</th><th>Males</th><th>Females</th><th>Total</th><th>Males</th><th>Females</th><th>Total</th></tr></thead><tbody><tr><td>Agriculture</td><td>35.4</td><td>22.3</td><td>31.4</td><td>27.2</td><td>15.6</td><td>23.5</td><td>24.4</td><td>12.6</td><td>20.6</td><td>19.8</td><td>11.2</td><td>16.9</td></tr><tr><td>Industry</td><td>47.4</td><td>37.8</td><td>44.5</td><td>50.1</td><td>36.4</td><td>45.7</td><td>49.6</td><td>38.3</td><td>45.9</td><td>52.6</td><td>35.8</td><td>47.1</td></tr><tr><td>Services</td><td>17.2</td><td>39.9</td><td>24.1</td><td>22.7</td><td>48.0</td><td>30.9</td><td>26.0</td><td>49.1</td><td>33.5</td><td>27.6</td><td>53.0</td><td>36.0</td></tr><tr><td>TOTAL</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015).
## Sectoral Shares in England (1381–1700) and Great Britain (1700–1870)

<table><thead><tr><th>Sector</th><th>1381</th><th>1522</th><th>1700</th><th>1759</th><th>1801</th><th>1851</th></tr></thead><tbody><tr><td>A. Output shares (%)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agriculture</td><td>45.5</td><td>39.7</td><td>26.7</td><td>29.7</td><td>31.3</td><td>18.7</td></tr><tr><td>Industry</td><td>28.8</td><td>38.7</td><td>41.3</td><td>35.2</td><td>32.7</td><td>32.1</td></tr><tr><td>Services</td><td>25.7</td><td>21.6</td><td>32.0</td><td>35.1</td><td>36.0</td><td>49.2</td></tr><tr><td>GDP</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr><tr><td>B. Labour-force shares (%)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agriculture</td><td>57.2</td><td>55.6</td><td>38.9</td><td>36.8</td><td>31.7</td><td>23.5</td></tr><tr><td>Industry</td><td>19.2</td><td>23.5</td><td>34.0</td><td>33.9</td><td>36.4</td><td>45.6</td></tr><tr><td>Services</td><td>23.6</td><td>20.9</td><td>27.2</td><td>29.3</td><td>31.9</td><td>30.9</td></tr><tr><td>GDP</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr><tr><td>C. Output per worker</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agriculture</td><td>79.5</td><td>71.4</td><td>68.7</td><td>80.8</td><td>98.9</td><td>79.6</td></tr><tr><td>Industry</td><td>150.1</td><td>164.5</td><td>121.5</td><td>103.8</td><td>89.7</td><td>70.3</td></tr><tr><td>Services</td><td>109.1</td><td>103.8</td><td>117.9</td><td>119.9</td><td>112.9</td><td>159.3</td></tr><tr><td>GDP</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2015).
## Sectoral annual growth rates in England (1381–1700) and Great Britain (1700–1870)

<table><thead><tr><th rowspan="3">Period</th><th colspan="9">Annual % growth</th></tr><tr><th colspan="3">Agriculture</th><th colspan="3">Industry</th><th colspan="3">Services</th><th colspan="2">GDP</th></tr><tr><th>Output</th><th>Labour-force</th><th>Labour-productivity</th><th>Output</th><th>Labour-force</th><th>Labour-productivity</th><th>Output</th><th>Labour-force</th><th>Labour-productivity</th><th>Output</th><th>Labour-force</th><th>Labour-productivity</th></tr></thead><tbody><tr><td>1381–1522</td><td>0.01</td><td>-0.01</td><td>0.02</td><td>0.27</td><td>0.10</td><td>0.17</td><td>0.06</td><td>-0.16</td><td>0.23</td><td>0.11</td><td>-0.02</td><td>0.14</td></tr><tr><td>1522–1700</td><td>0.38</td><td>0.25</td><td>0.13</td><td>0.73</td><td>0.66</td><td>0.07</td><td>0.74</td><td>0.60</td><td>0.14</td><td>0.60</td><td>0.45</td><td>0.16</td></tr><tr><td>1700–1759</td><td>0.79</td><td>0.22</td><td>0.57</td><td>0.63</td><td>0.31</td><td>0.32</td><td>0.70</td><td>0.44</td><td>0.26</td><td>0.69</td><td>0.32</td><td>0.38</td></tr><tr><td>1759–1801</td><td>0.85</td><td>0.44</td><td>0.41</td><td>1.54</td><td>0.97</td><td>0.57</td><td>1.36</td><td>1.00</td><td>0.36</td><td>1.23</td><td>0.79</td><td>0.44</td></tr><tr><td>1801–1851</td><td>0.74</td><td>0.64</td><td>0.10</td><td>3.00</td><td>1.74</td><td>1.23</td><td>2.16</td><td>1.45</td><td>0.71</td><td>2.10</td><td>1.35</td><td>0.74</td></tr><tr><td>1381–1759</td><td>0.30</td><td>0.13</td><td>0.17</td><td>0.54</td><td>0.40</td><td>0.14</td><td>0.48</td><td>0.31</td><td>0.17</td><td>0.43</td><td>0.25</td><td>0.18</td></tr><tr><td>1759–1851</td><td>0.79</td><td>0.54</td><td>0.24</td><td>2.33</td><td>1.39</td><td>0.93</td><td>1.80</td><td>1.24</td><td>0.55</td><td>1.70</td><td>1.09</td><td>0.60</td></tr></tbody></table>

Source: Broadberry et al. (2015).
# Structural change in Britain

<figure><img src="image_87.png" /><figcaption>## Sectoral Shares in Nominal GDP in England (1381–1700) and Great Britain (1700–1870)</figcaption></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015).
## Sectoral Shares in Labor Force in England (1381–1700) and Great Britain (1700–1870)

<figure><img src="image_88.png" /></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015).
* As early as 1381, agriculture < 60% of GDP and labor, and 30% in 1801.

* 1500s–1700, productivity growth slows down in industry due to labor-intensive character of industry.

* 1700–1759: agriculture productivity grows (agricultural revolution) and in industry.

* During IR, industry productivity grows due to labor-saving technologies and division of labor.

* Shift of labor from agriculture to industry occurred before 1759 due to increased productivity in agriculture.

⇒ Labor-intensive (proto) industrialization in 1522–1759 without industrial productivity growth.
* Real wages offer a complementary view of long-run changes in living standards.

* Usually focus on day wages, with assumptions on yearly days of work.

* “Golden Age of Labor” after the Black Death.

* Decline then stagnation from 1650s the 1800s (“Engels' Pause”).

* Puzzle: why divergence with respect to GDP per capita trends from Broadberry et al. (2015) (besides changes in labor supply in 18th century)?
<figure><img src="image_89.png" /><figcaption>## Real wages in England</figcaption></figure>

Source: Angeles (2008) based on Clark (2005).
<figure><img src="image_90.png" /><figcaption>Real wages in England</figcaption></figure>

Source: Angeles (2008) based on Clark (2007).
<figure><img src="image_91.png" /><figcaption>Real GDP per capita (log scale, 1700=100)</figcaption></figure>

Source: Broadberry et al. (2015)
## Humphries and Weisdorf (2019)

* Revisit stylized facts with annual wages.

* Downscale the “Golden Age of Labor”.

* Push the take-off 200 years, to late 1500s.

* Main reason: the “Industrious Revolution” was much earlier than thought based on Voth (2000, 2001).

* The assumption of 250 working days was wrong: labor input on the intensive margin varied widely in the past.
* Annual wage contracts were common, especially in agriculture.

* If workers are homogeneous and mobile (non-segmented labor market), then yearly contracts in agriculture relevant to workers on daily contracts in all sectors.
* Data sources: 13 primary and secondary sources for unskilled annual male workers in agriculture.

* Data points: 6,800 annual payments, with good temporal and geographical coverage (1260s–1850s) across various occupations.

* In-kind transfers: common, so assume provision of “respectability” basket based on Allen (2009) for 2,500 calories per day.
Allen's Consumption Baskets for One Adult Person

<table><thead><tr><th colspan="2">Respectability</th><th colspan="2">Bare bones subsistence</th></tr><tr><th>Good</th><th>Quantities per year</th><th>Good</th><th>Quantities per year</th></tr></thead><tbody><tr><td>Bread</td><td>234 kg</td><td>Oatmeal</td><td>170 kg</td></tr><tr><td>Beans/peas</td><td>52 L</td><td>Beans/peas</td><td>34 kg</td></tr><tr><td>Meat</td><td>26 kg</td><td>Meat</td><td>5 kg</td></tr><tr><td>Butter</td><td>5.2 kg</td><td>Butter</td><td>3 kg</td></tr><tr><td>Cheese</td><td>5.2 kg</td><td>Cheese</td><td></td></tr><tr><td>Eggs</td><td>52 each</td><td>Eggs</td><td></td></tr><tr><td>Beer</td><td>182 L</td><td>Beer</td><td></td></tr><tr><td>Soap</td><td>2.6 kg</td><td>Soap</td><td>1.3 kg</td></tr><tr><td>Linen</td><td>5 m</td><td>Linen</td><td>3 m</td></tr><tr><td>Candles</td><td>2.6 kg</td><td>Candles</td><td>1.3 kg</td></tr><tr><td>Lamp oil</td><td>2.6 L</td><td>Lamp oil</td><td>1.3 kg</td></tr><tr><td>Fuel</td><td>5.0 M BTU</td><td>Fuel</td><td>2.0 M BTU</td></tr><tr><td>Rent</td><td>5% allowance</td><td>Rent</td><td>5% allowance</td></tr><tr><td>Total</td><td>2,500 cal/day</td><td>Total</td><td>2,100 cal/day</td></tr></tbody></table>

Source: Humphries and Weisdorf (2019) based on Allen (2009, 2015)
## Number of Annual Payments by Region and Occupational Category

<table><thead><tr><th>Occupation</th><th>Frequency</th><th>Per cent</th><th>Cum.</th><th>Region</th><th>Frequency</th><th>Per cent</th><th>Cum.</th></tr></thead><tbody><tr><td>Men/Helpers</td><td>2,730</td><td>40</td><td>40</td><td>South</td><td>2,334</td><td>34</td><td>34</td></tr><tr><td>Servants</td><td>865</td><td>13</td><td>53</td><td>Midlands</td><td>3,516</td><td>52</td><td>86</td></tr><tr><td>Labourers</td><td>559</td><td>8</td><td>61</td><td>North</td><td>950</td><td>14</td><td>100</td></tr><tr><td>Unknown</td><td>2,646</td><td>39</td><td>100</td><td></td><td></td><td></td><td></td></tr><tr><td>All</td><td>6,800</td><td>100</td><td></td><td>All</td><td>6,800</td><td>100</td><td></td></tr></tbody></table>

Source: Humphries and Weisdorf (2019)
* Account for occupational and spatial heterogeneity in data sources by demeaning through OLS regressions by periods of 50 and 100 years:

$$ \ln(\text{Wage}_{it}) = \alpha_i + \sum_j \gamma_j \text{Work}_j + \sum_k \eta_k \text{Region}_k + \sum_l \theta_l \text{Decade}_l + \varepsilon_{it} $$
* Account for occupational and spatial heterogeneity in data sources by demeaning through OLS regressions by periods of 50 and 100 years:

$$ \ln(\text{Wage}_{it}) = \alpha_i + \sum_j \gamma_j \text{Work}_j + \sum_k \eta_k \text{Region}_k + \sum_l \theta_l \text{Decade}_l + \varepsilon_{it} $$

* Predict payments using regression coefficients and applying weights by share of observations within each occupational category and region population shares.
* Account for occupational and spatial heterogeneity in data sources by demeaning through OLS regressions by periods of 50 and 100 years:

$$ \ln(\text{Wage}_{it}) = \alpha_i + \sum_j \gamma_j \text{Work}_j + \sum_k \eta_k \text{Region}_k + \sum_l \theta_l \text{Decade}_l + \varepsilon_{it} $$

* Predict payments using regression coefficients and applying weights by share of observations within each occupational category and region population shares.

* Then deflate the series using a standard historic consumer price index.
* Account for occupational and spatial heterogeneity in data sources by demeaning through OLS regressions by periods of 50 and 100 years:

$$ \ln(\text{Wage}_{it}) = \alpha_i + \sum_j \gamma_j \text{Work}_j + \sum_k \eta_k \text{Region}_k + \sum_l \theta_l \text{Decade}_l + \varepsilon_{it} $$

* Predict payments using regression coefficients and applying weights by share of observations within each occupational category and region population shares.

* Then deflate the series using a standard historic consumer price index.

* Normalize the series to “welfare ratios” (number of respectability baskets that income can buy).
<figure><img src="image_92.png" /><figcaption>National Real Annual Incomes, 1260–1850</figcaption></figure>

Source: Humphries and Weisdorf (2019)
* New stylized facts:

* “Golden Age of Labor” after the Black Death is downscaled.

* Post-Golden Age downturn later, in 1550s rather than 1450s.

* Gains after 1600s (no “Engel’s Pause”).

* Only after 1700s that reach 2 baskets.

* Only after 1800s that can supper a family of 4 ⇌ the male breadwinner model was irrelevant before then.
<figure><img src="image_93.png" /><figcaption>## Indices of GDP per Capita and Real Annual Income, 1260–1850</figcaption></figure>

Source: Humphries and Weisdorf (2019)
* Puzzle resolved: annual wages track GDP per capita quite well.

* Constant labor supply with daily wage series is misguided.
* Puzzle resolved: annual wages track GDP per capita quite well.

* Constant labor supply with daily wage series is misguided.

* Other discrepancies can be due to changes in labor share (ratio income to GDP per capita).
<figure><img src="image_94.png" /><figcaption>## Length of Working Year, 1260–1850</figcaption></figure>

Source: Humphries and Weisdorf (2019)
<figure><img src="image_95.png" /><figcaption>Labor Share (income / GDP per capita), 1260–1850</figcaption></figure>

Source: Humphries and Weisdorf (2019)
* The puzzle of the divergence between wage data and GDP per capita

* Very strong variation in length of working days per year.

* Assumption of 250 days worked is unwarranted.

* People worked much less post-Black Death.

* Industrious Revolution started much earlier than 18th century.

* Explanation by Angeles (2007) holds.

⇒ Puzzle solved!
Continental Europe
<figure><img src="image_96.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
Alvarez-Nogla and Prados de la Escosura (2013)

* Two regimes in pre-industrial Spain:

* 1270s–1590s: high land-labor ratio frontier and pastoral economy.

* 1270s–1340s: sustained growth interrupted by Black Death (1348) and Spanish phase of Hundred Years War (1365–89).

* 1390s–1590s: growth resumes driven by international trade, interrupted by political turmoil in 1490s.

* 1600s–1810s: low-wage, densely populated, lower path economy.
Alvarez-Nogla and Prados de la Escosura (2013)

* Two regimes in pre-industrial Spain:

* 1270s–1590s: high land-labor ratio frontier and pastoral economy.

* 1270s–1340s: sustained growth interrupted by Black Death (1348) and Spanish phase of Hundred Years War (1365–89).

* 1390s–1590s: growth resumes driven by international trade, interrupted by political turmoil in 1490s.

* 1600s–1810s: low-wage, densely populated, lower path economy.

* Estimates of agricultural output from indirect demand approach, the rest through changes in urban population.
<figure><img src="image_97.png" /><figcaption>The Reconquest in Spain</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013) based on Mackay (1977).
* Indirect estimation of agricultural output through demand function approach. Allen (2000)

$$Q^A = rcN$$

* Q^A: real agricultural output.

* r = Q^A/C^A: ratio of production to consumption (often r = 1).

* c: per capita consumption.

* N: population.
* Real consumption per capita:

$$C = (P^A/P)^{\alpha_1} (P^{NA}/P)^{\alpha_2} y^{\beta}$$

$$\ln c = \alpha_0 + \alpha_1 \ln (P^A/P) + \alpha_2 \ln (P^{NA}/P) + \beta \ln y$$

* $P^A/P$: agricultural product prices in real terms.

* $P^{NA}/P$: non-agricultural product prices in real terms.

* y: real income per capita.

* $\alpha_0$: constant.

* $\alpha_1$: own-price elasticity of demand.

* $\alpha_2$: cross-price elasticity of demand.

* $\beta$: income elasticity of demand.
* Prices are known.

* Income is unknown ⇒ use real wage w as a proxy (for workers in construction: masons, bricklayers, tilers, carpenters).

* Other sources of income, especially in rural areas?

* Share of labor in national income?

* Wage rates are available ⇒ working days per year?
* Prices are known.

* Income is unknown ⇒ use real wage w as a proxy (for workers in construction: masons, bricklayers, tilers, carpenters).

* Other sources of income, especially in rural areas?

* Share of labor in national income?

* Wage rates are available ⇒ working days per year?

* Price and income elasticities from current developing countries (adapted to case of Spain).

* $\alpha_1 = -0.4$.

* $\alpha_2 = 0.1$.

* $\therefore \beta = 0.3$.
Real wage rates, 1277-1850 (1790-9=100) (logs)

<figure><img src="image_98.png" /></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
* Trends in real wage rates:

* 1290s–1340s: rise.

* 1350s–1390s: sharp decline following Black Death and Hundred Years War.

* 1400s–1450s: recovery and millennium peak.

* 1460s—1650s: long-term decline (below pre-plague levels).

* 1660s–1790s: flat and declining trend.
* Is real wage a good proxy for real income per capita?

* Probably 120 working days per hour initially, rising to 150 at the end.

* Share of labor in national income fluctuated according to land rent to wage rate ratio.

* Generally high inequality: 10% richest were 15 times richer than 40% poorest (close to England and France).
<figure><img src="image_99.png" /><figcaption>Land rent-wage rate ratio (1790-9=100) (logs)</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
* Uncertainty in estimation, so three possibilities:

* Keep equation as is.

* Assume response of labor supply to change in wages so returns to labor stable over time (drop the income elasticity term).

* Use weighted average of real wage rates and real land rents for income proxy.
<figure><img src="image_100.png" /><figcaption>Real consumption per capita of agricultural goods (1850-9=100) (logs)</figcaption></figure>

Source: Álvarez-Nogal and Prados de la Escosura (2013).
* Choice of series:

* Series II: convergence post-1550s but implies lower labor supply before given high wage rates, which seems implausible.

* Series I and III: convergence throughout, suggesting important roles of relative prices.

* Use series III for 1320–1845, and series I for 1230–1320 (no rent data).
* Long-run trends:

* 1230s–1340s: high pre-plague consumption and peak (due to Reconquest of land without demographic expansion).

* 1340s–1370s: fall post-plage.

* 1370s–1450s: recovery.

* 1450s–1650s: long-trend decline.

* 1650s–1800s: stable with slight recovery and decline.
* Long-run trends:

* 1230s–1340s: high pre-plague consumption and peak (due to Reconquest of land without demographic expansion).

* 1340s–1370s: fall post-plage.

* 1370s–1450s: recovery.

* 1450s–1650s: long-trend decline.

* 1650s–1800s: stable with slight recovery and decline.

* Get agricultural output by multiplying by population (given agricultural export close to 0).
* For industry and services: proxy with urbanization rate (share of population in urban centers).

* Estimate non-agricultural output $Q^{NA}$:

$$Q = Q^A + Q^{NA} \iff Q = \frac{Q^A}{1 - (Q^{NA}/Q)}$$

* $Q^{NA}/Q$: share non-agricultural output.

* Use a 5,000 inhabitants threshold (consistent with Bairoch's (1988) estimates).

* Exclude known “agro-towns” in the frontier economy of southern Spain.
Adjusted rate of urbanization (%)

<table><tr><td>1000</td><td>8.0</td></tr><tr><td>1300</td><td>8.8</td></tr><tr><td>1400</td><td>7.8</td></tr><tr><td>1530</td><td>9.9</td></tr><tr><td>1591</td><td>14.5</td></tr><tr><td>1700</td><td>11.1</td></tr><tr><td>1750</td><td>13.5</td></tr><tr><td>1787</td><td>17.4</td></tr><tr><td>1857</td><td>23.2</td></tr></table>

Note: * Share of population in towns of 5,000 and over, excluding those living on agriculture.

Source: Alvarez-Nogal and Prados de la Escosura (2013).
* Are the fall in consumption per capita and increase urbanization contradictory?

* Hypothesis: non-agriculture consumption increased at expense of food staples, due to increased opportunity cost of food.

* Confirmed by decline of price of industrial goods relative to agricultural goods.
<figure><img src="image_101.png" /><figcaption>Ratio of industrial to agricultural prices (logs)</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
* Final step: compute GDP per capita by weighting agricultural and non-agricultural output.

* Use relative price indexes in both sectors.
<figure><img src="image_102.png" /><figcaption>Share of agriculture in GDP (current prices) (%)</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
<figure><img src="image_103.png" /><figcaption>Real output per capita (1850–9=100) (logs)</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
* Two regimes in pre-industrial Spain:

* 1270s–1590s: high land-labor ratio frontier and pastoral economy.

* 1270s–1340s: sustained growth interrupted by Black Death (1348, despite mild population effects 25–35%) and Spanish phase of Hundred Years War (1365–89).

* 1390s–1590s: growth resumes driven by international trade, interrupted by political turmoil in 1490s.

* 1600s–1810s: low-wage, densely populated, lower path economy. Initial fall driven by contraction of purchasing power of American silver and rising cost of empire.
* Two regimes in pre-industrial Spain:

* 1270s–1590s: high land-labor ratio frontier and pastoral economy.

* 1270s–1340s: sustained growth interrupted by Black Death (1348, despite mild population effects 25–35%) and Spanish phase of Hundred Years War (1365–89).

* 1390s–1590s: growth resumes driven by international trade, interrupted by political turmoil in 1490s.

* 1600s–1810s: low-wage, densely populated, lower path economy. Initial fall driven by contraction of purchasing power of American silver and rising cost of empire.

* Differences in well-being as indicated by inequality (ratio of output per capita to nominal wages)?
Inequality (1850–9=100) (logs)

<figure><img src="image_104.png" /></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
Output per capita in western Europe (UK in 1850 = 100)

<table><thead><tr><th></th><th>UK</th><th>Netherlands</th><th>Germany</th><th>France</th><th>Italy</th><th>Spain</th><th>Sweden</th></tr></thead><tbody><tr><td colspan="8">Panel A. Relative per capita GDP in 1850 at current US relative prices (PPP) (UK = 100)</td></tr><tr><td>1300</td><td>25</td><td></td><td></td><td></td><td>72</td><td>51</td><td></td></tr><tr><td>1348</td><td>26</td><td>22</td><td></td><td></td><td>67</td><td>54</td><td></td></tr><tr><td>1400</td><td>38</td><td>31</td><td></td><td>52</td><td>78</td><td>48</td><td></td></tr><tr><td>1500</td><td>39</td><td>37</td><td>49</td><td>50</td><td>68</td><td>50</td><td></td></tr><tr><td>1570</td><td>39</td><td>37</td><td></td><td></td><td>64</td><td>54</td><td>35</td></tr><tr><td>1600</td><td>37</td><td>68</td><td>34</td><td>50</td><td>60</td><td>53</td><td></td></tr><tr><td>1650</td><td>34</td><td>69</td><td></td><td></td><td>62</td><td>41</td><td></td></tr><tr><td>1700</td><td>55</td><td>54</td><td>40</td><td>54</td><td>65</td><td>48</td><td></td></tr><tr><td>1750</td><td>61</td><td>60</td><td>45</td><td>55</td><td>68</td><td>46</td><td></td></tr><tr><td>1800</td><td>75</td><td>67</td><td>42</td><td>56</td><td>60</td><td>54</td><td>41</td></tr><tr><td>1850</td><td>100</td><td>79</td><td>61</td><td>78</td><td>66</td><td>64</td><td>52</td></tr><tr><td colspan="8">Panel B. Relative per capita GDP in 1850 at 1990 international prices (PPP) (UK = 100)</td></tr><tr><td>1300</td><td>25</td><td></td><td></td><td></td><td>66</td><td>37</td><td></td></tr><tr><td>1348</td><td>26</td><td>29</td><td></td><td></td><td>62</td><td>39</td><td></td></tr><tr><td>1400</td><td>38</td><td>39</td><td></td><td>46</td><td>72</td><td>35</td><td></td></tr><tr><td>1500</td><td>39</td><td>48</td><td>49</td><td>44</td><td>63</td><td>36</td><td></td></tr><tr><td>1570</td><td>39</td><td>47</td><td></td><td></td><td>60</td><td>39</td><td>29</td></tr><tr><td>1600</td><td>37</td><td>88</td><td>35</td><td>44</td><td>56</td><td>38</td><td></td></tr><tr><td>1650</td><td>34</td><td>89</td><td></td><td></td><td>57</td><td>29</td><td></td></tr><tr><td>1700</td><td>55</td><td>69</td><td>40</td><td>47</td><td>60</td><td>35</td><td></td></tr><tr><td>1750</td><td>61</td><td>78</td><td>45</td><td>48</td><td>63</td><td>34</td><td></td></tr><tr><td>1800</td><td>75</td><td>86</td><td>42</td><td>49</td><td>56</td><td>39</td><td>34</td></tr><tr><td>1850</td><td>100</td><td>102</td><td>61</td><td>60</td><td>61</td><td>46</td><td>44</td></tr></tbody></table>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
* Other estimates:

* France, Italy, Germany: demand approach for agriculture + urbanization for the rest.

* Holland, Britain: output approach based on van Zanden and van Leuwen (2012) and Broadberry et al (2011).

* Prefer estimates using 1990 international (GKI) dollars from Maddison for PPP conversions.
<figure><img src="image_105.png" /><figcaption>GDP per Capita in Europe, 1270-1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
* Turning points:

* Black Death: downturn due to characteristics of frontier economy with low baseline demographic dynamics.

* 1600s decline: drop from UK and trends down relative to Europe.

* Generally, Spain doesn't face the Malthusian trap because of low population density: population expansion associated with positive per capita growth.

* Italy is the polar opposite case.
<figure><img src="image_106.png" /><figcaption>Real output per capita in Spain and Italy (1850–9 = 100) (logs)</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2013).
Alvarez-Nogla and Prados de la Escosura (2023, WP)

* Very large decline in 1570s–1650s.

* Impact on people's welfare?
* Very large decline in 1570s–1650s.

* Impact on people's welfare?

* Measure well-being through people's “spiritual satisfaction” in a country where religion had central role.

* Use the Bulls of the Crusade: alms collected by Spanish Monarchy after 1574 for 2 silver Reales (and 8 for the wealthy).

* Spiritual benefit: plenary indulgencies that erased the penance for guilt after sinning, reducing days of Purgatory.

* Measure of spiritual subjective well-being: number of bulls sold relative to population.

* Focus on the effect of acquiring bulls rather than motive.
A Bull of the Crusade

<figure><img src="image_107.png" /></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2023).
* Archival sources:

* Number of bulls sold yearly 1574–1704.

* Based on accounting books on the Crusade across various regional archives.

* Broad temporal and geographical coverage: Crown of Castile, Aragon and Navarre.

* Compile individual bulls printed for each bishopric and subtract unsold bulls.
Total bulls sold

<figure><img src="image_108.png" /></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2023).
* Main issue: though cheap, depends on purchasing power.

* Argue that link between bulls sold and economic hardship is uninformative of individuals' subjective welfare.

* Purchase was cheap and provided spiritual comfort by removing all guilt.

* Deviation of bulls sold (per capita) and population is informative.
<figure><img src="image_109.png" /><figcaption>Annual total bulls and population (normalized)</figcaption></figure>

Source: Álvarez-Nogal and Prados de la Escosura (2023).
<figure><img src="image_110.png" /><figcaption>Subjective well-being: ratio total bulls to population (normalized, logs)</figcaption></figure>

Source: Alvarez-Nogal and Prados de la Escosura (2023).
* Major shocks correlate only slightly with bull movements:

* Famine episodes: 1606, 1647–50, 1684.

* Internal political turmoil and wars.

* Cannot explain contractions of 1575–86 and 1626.

* Some potential explanations:

* 1575–86: credit crisis, when monarchy stopped payment of debt.

* 1626: alterations to monetary policy, when monarchy substituted copper for silver coinage, leading to scarcity of silver (needed to pay bulls).
* Potential explanations for episodes of improvements:

* 1602: recovery from plague in Castile.

* 1670–82: period of expansion resulting from the end of war with Catalonia (1652) and Portugal (1668).
* Potential explanations for episodes of improvements:

• 1602: recovery from plague in Castile.

• 1670–82: period of expansion resulting from the end of war with Catalonia (1652) and Portugal (1668).

⇒ Not great measure, but interesting and innovative way to think about subjective well-being in historical setting.
Italy
<figure><img src="image_111.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
Malanima (2011)

* GDP estimates for Italy: Center and North.

* Italy not unified until 1861, little data in South.

* Use indirect method through demand side for agricultural output.

* For other output, urbanization proxy method.
* Indirect estimation of agricultural output through demand function approach. Allen (2000)

$$Q^A = rcN$$

* Q^A: real agricultural output.

* r = Q^A/C^A: ratio of production to consumption (often r = 1).

* c: per capita consumption.

* N: population.
* Real consumption per capita:

$$C = (P^A/P)^{\alpha_1} (P^{NA}/P)^{\alpha_2} y^{\beta}$$

$$\ln c = \alpha_0 + \alpha_1 \ln (P^A/P) + \alpha_2 \ln (P^{NA}/P) + \beta \ln y$$

* $P^A/P$: agricultural product prices in real terms.

* $P^{NA}/P$: non-agricultural product prices in real terms.

* y: real income per capita.

* $\alpha_0$: constant.

* $\alpha_1$: own-price elasticity of demand.

* $\alpha_2$: cross-price elasticity of demand.

* $\beta$: income elasticity of demand.
* Prices are known (Tuscany and Lombardy, argued representative).

* Population is known.

* Income is unknown ⇒ use real wage w as a proxy (laborers for rural wages, masons for urban wages).

* Other sources of income, especially in rural areas? Assume competition, so equalization.

* Share of labor in national income? Assume constant 70% (based on mid-19th century data).

* Wage rates are available ⇒ working days per year? Calculate on assumptions in labor supply curve.
* Prices are known (Tuscany and Lombardy, argued representative).

* Population is known.

* Income is unknown ⇒ use real wage w as a proxy (laborers for rural wages, masons for urban wages).

* Other sources of income, especially in rural areas? Assume competition, so equalization.

* Share of labor in national income? Assume constant 70% (based on mid-19th century data).

* Wage rates are available ⇒ working days per year? Calculate on assumptions in labor supply curve.

* Same price and income elasticities from current developing countries (adapted to case of Italy).
<figure><img src="image_112.png" /><figcaption>Price indices of agricultural and non-agricultural goods (1420-40=1, logs)</figcaption></figure>

Source: Malanima (2011).
<figure><img src="image_113.png" /><figcaption>Population in Italy and central-northern Italy (C-N)</figcaption></figure>

Source: Malanima (2011).
Implicit working days per year

<table><thead><tr><th></th><th>Days of work</th></tr></thead><tbody><tr><td>1350-1400</td><td>161</td></tr><tr><td>1400-1500</td><td>120</td></tr><tr><td>1450-1500</td><td>136</td></tr><tr><td>1500-1600</td><td>206</td></tr><tr><td>1550-1600</td><td>250</td></tr><tr><td>1600-1700</td><td>204</td></tr><tr><td>1650-1700</td><td>169</td></tr><tr><td>1700-1800</td><td>165</td></tr><tr><td>1750-1800</td><td>200</td></tr><tr><td>1800-200</td><td>210</td></tr></tbody></table>

Source: Malanima (2011).
Index of real wage rates (1420-40=1, logs)

<figure><img src="image_114.png" /></figure>

Source: Malanima (2011).
<figure><img src="image_115.png" /><figcaption>Index of per capita agricultural GDP (1420-40=1, logs)</figcaption></figure>

Source: Malanima (2011).
* For industry and services: proxy with urbanization rate (share of population in urban centers).

* Estimate non-agricultural output $Q^{NA}$:

$$Q = Q^A + Q^{NA} \iff Q = \frac{Q^A}{1 - (Q^{NA}/Q)}$$

* $Q^{NA}/Q$: share non-agricultural output.

* Use a 5,000 inhabitants threshold (consistent with Bairoch's (1988) estimates).
Urbanization (%)

<table><thead><tr><th></th><th>Urbanisation<br>(>5,000)</th></tr></thead><tbody><tr><td>1300</td><td>21.4</td></tr><tr><td>1350</td><td>17.7</td></tr><tr><td>1400</td><td>17.6</td></tr><tr><td>1450</td><td>17.0</td></tr><tr><td>1500</td><td>21.0</td></tr><tr><td>1550</td><td>20.0</td></tr><tr><td>1600</td><td>18.4</td></tr><tr><td>1650</td><td>15.2</td></tr><tr><td>1700</td><td>16.9</td></tr><tr><td>1750</td><td>17.7</td></tr><tr><td>1800</td><td>17.5</td></tr><tr><td>1850-61</td><td>16.2</td></tr></tbody></table>

Source: Malanima (2011).
<figure><img src="image_116.png" /><figcaption>Per capita GDP in central and northern Italy (1420-40=1)</figcaption></figure>

Source: Malanima (2011).
* Three epoch of Italy's pre-industrial growth:

* 1300s-1550s: Italian Renaissance. Millennium high GDP per capita, Europe's leader (with NL).

* 1550s-1880s: Italian decline.

* After 1880s: beginning of modern growth in Italy.
Chilosi and Ciccarelli (2023)

* Two criticism of Malanima's (2011) series:

* No estimate for South Italy (44% of area and 36% of population).

* Demand-approach based on daily wages, not representative (misses industrious revolution).

* New estimates (incl. South) independent of wages:
Chilosi and Ciccarelli (2023)

* Two criticism of Malanima's (2011) series:

* No estimate for South Italy (44% of area and 36% of population).

* Demand-approach based on daily wages, not representative (misses industrious revolution).

* New estimates (incl. South) independent of wages:

* Use Engel's law: as GDP ↑, share spent on food ↓, and so agricultural employment share ↓.

* Infer GDP per capita from occupational structure.

* Approach used in England, showing predictions close to Broadberry et al. (2015).
Chilosi and Ciccarelli (2023)

* Two criticism of Malanima's (2011) series:

* No estimate for South Italy (44% of area and 36% of population).

* Demand-approach based on daily wages, not representative (misses industrious revolution).

* New estimates (incl. South) independent of wages:

* Use Engel's law: as GDP ↑, share spent on food ↓, and so agricultural employment share ↓.

* Infer GDP per capita from occupational structure.

* Approach used in England, showing predictions close to Broadberry et al. (2015).

* Findings: downward upwards for C-N Italy and positive growth for S Italy.
* Method exploits Engel's law to reconstruct GDP per capita trends consistent with agricultural employment shares. Groth and Persson (2016)

* Develop a general equilibrium model linking labor income in agriculture to labor share in agriculture.

* Data from 354 regional censuses between 1800 and 1859. Chilosi and Ciccarelli (2022)
<figure><img src="image_117.png" /><figcaption>Number of censuses with occupations per province (1800–61)</figcaption></figure>
<figure><img src="image_118.png" /><figcaption>Sectoral employment shares in Italy (1800–61)</figcaption></figure>

Source: Chilosi and Ciccarelli (2022).
<figure><img src="image_119.png" /><figcaption>Agricultural employment shares by province in Italy (1848)</figcaption></figure>

Source: Chilosi and Ciccarelli (2022).
Yearly changes in agricultural employment shares by province in Italy (1800–61)

<figure><img src="image_120.png" /></figure>

Source: Chilosi and Ciccarelli (2022).
* Sectoral shares before 1800:

* Extrapolate agricultural employment share backwards relying on trends in urbanization rates.

* Assume distribution of agricultural workers across cities and countryside did not change.

* Little proto-industrialization in Italy.

* Account for agro-towns by focusing on 10,000 threshold
Trends in urbanization rates in Italy (1400-61)

(a) 5 000 threshold

<figure><img src="image_121.png" /></figure>

(b) 10 000 threshold

<figure><img src="image_122.png" /></figure>

<figure><img src="image_123.png" /></figure>

Source: Chilosi and Ciccarelli (2022).
<figure><img src="image_124.png" /><figcaption>Trends in agricultural employment shares in Italy (1400–61)</figcaption></figure>

Source: Chilosi and Ciccarelli (2022).
<figure><img src="image_125.png" /><figcaption>GDP per capita in Italy (2011 PPP IGK $, log scale)</figcaption></figure>

Source: Chilosi and Ciccarelli (2023).
* New information for the South:

* 1400–1600: strong growth (+33%) during Renaissance

* 1600–1700: century-long contraction (–10%).

* 1700–1861: slow growth (+.01%) during Long Risorgimento.
* New information for the South:

* 1400–1600: strong growth (+33%) during Renaissance

* 1600–1700: century-long contraction (−10%).

* 1700–1861: slow growth (+.01%) during Long Risorgimento.

* Comparison with Malanima's (2011) series:

* Similar: long stagnation for C-N Italy from 1500 to 1861.

* Lower levels in 1400–1500: unclear but ↓ agricultural shares incompatible with 15th-century decline.

* Higher levels in 1700–1770: discounts changes in working days.

* Overall, long-run stagnation of C-N of Italy.
<figure><img src="image_126.png" /><figcaption>Labor participation index (1630s=100)</figcaption></figure>

Source: Chilosi and Ciccarelli (2023).
<figure><img src="image_127.png" /><figcaption>GDP per capita (2011 PPP IGK$, log scale)</figcaption></figure>

Source: Chilosi and Ciccarelli (2023).
France
<figure><img src="image_128.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2021) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), and Alvarez-Nogal and Prados de la Escosura (2013).
## GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)

<figure><img src="image_129.png" /></figure>

Source: Broadberry (2020) based on Palma and Reis (2019), Ridolfi (2016), Krantz (2017), Schon and Krantz (2012), Malinowski and van Zanden (2017).
Ridolfi and Nuvolari (2021)

* Interesting features of the French case:

* Typical case of stagnant economy dominated by Malthusian checks (*histoire immobile*?).

* Intermediate position between North and South in the Little Divergence?
Ridolfi and Nuvolari (2021)

* Interesting features of the French case:

* Typical case of stagnant economy dominated by Malthusian checks (*histoire immobile*?).

* Intermediate position between North and South in the Little Divergence?

* No “new generation” estimates for France.

* Broadberry's series relies on Ridolfi's (2016) dissertation draft.

* Ridolfi and Nuvolari (2021) update these estimates.
* Indirect estimation of agricultural output through demand function approach. Allen (2000)

$$Q^A = rcN$$

* Q^A: real agricultural output.

* r = Q^A/C^A: ratio of production to consumption (often r = 1).

* c: per capita consumption.

* N: population.
* Real consumption per capita:

$$C = (P^A/P)^{\alpha_1} (P^{NA}/P)^{\alpha_2} y^{\beta}$$

$$\ln c = \alpha_0 + \alpha_1 \ln (P^A/P) + \alpha_2 \ln (P^{NA}/P) + \beta \ln y$$

* $P^A/P$: agricultural product prices in real terms.

* $P^{NA}/P$: non-agricultural product prices in real terms.

* y: real income per capita.

* $\alpha_0$: constant.

* $\alpha_1$: own-price elasticity of demand.

* $\alpha_2$: cross-price elasticity of demand.

* $\beta$: income elasticity of demand.
* Prices are known (47k quotes for 12 commodities). Ridolfi (2019)

* Population is known. Dupâquier (1988)

* Income is unknown ⇒ use real wage $w$ as a proxy (laborers for rural wages, building's craftsmen for urban wages). Ridolfi (2019)

* Representative? Apply labor share weights.

* In-kind transfers: classify wages depending on in-kind payments, keeping only non-nourished workers.

* Deflate nominal wages by cost of living index based on respectability basket with time-varying weights.

* Wage rates are available ⇒ working days per year? Convert to annual income assuming 250 days per year (accurate for construction workers, assumed for laborers).
* Prices are known (47k quotes for 12 commodities). Ridolfi (2019)

* Population is known. Dupâquier (1988)

* Income is unknown ⇒ use real wage $w$ as a proxy (laborers for rural wages, building's craftsmen for urban wages). Ridolfi (2019)

* Representative? Apply labor share weights.

* In-kind transfers: classify wages depending on in-kind payments, keeping only non-nourished workers.

* Deflate nominal wages by cost of living index based on respectability basket with time-varying weights.

* Wage rates are available ⇒ working days per year? Convert to annual income assuming 250 days per year (accurate for construction workers, assumed for laborers).

* Same price and income elasticities from current developing countries (adapted to case of France).
<figure><img src="image_130.png" /><figcaption>French nominal wages (Sous tournois per day, log scale)</figcaption></figure>

Source: Ridolfi (2019).
Structure of consumer price index

<table><thead><tr><th>Good</th><th>Quantity per person per year</th><th>Unit</th><th>1300-1400</th><th>1400-1500</th><th>1500-1600</th><th>1600-1700</th><th>1700-1800</th><th>1800-1850</th></tr></thead><tbody><tr><td>Bread</td><td>182</td><td>kg</td><td>0.340</td><td>0.322</td><td>0.365</td><td>0.362</td><td>0.354</td><td>0.360</td></tr><tr><td>Legumes</td><td>52</td><td>l</td><td>0.047</td><td>0.057</td><td>0.085</td><td>0.073</td><td>0.066</td><td>0.066</td></tr><tr><td>Beef</td><td>26</td><td>kg</td><td>0.095</td><td>0.095</td><td>0.103</td><td>0.115</td><td>0.121</td><td>0.151</td></tr><tr><td>Butter</td><td>5.2</td><td>kg</td><td>0.060</td><td>0.059</td><td>0.055</td><td>0.060</td><td>0.057</td><td>0.056</td></tr><tr><td>Cheese</td><td>5.2</td><td>kg</td><td>0.039</td><td>0.039</td><td>0.036</td><td>0.040</td><td>0.035</td><td>0.037</td></tr><tr><td>Eggs</td><td>52</td><td>unit</td><td>0.016</td><td>0.010</td><td>0.014</td><td>0.018</td><td>0.018</td><td>0.013</td></tr><tr><td>Wine</td><td>182</td><td>l</td><td>0.152</td><td>0.179</td><td>0.119</td><td>0.104</td><td>0.104</td><td>0.095</td></tr><tr><td>Soap</td><td>2.6</td><td>kg</td><td>0.037</td><td>0.034</td><td>0.036</td><td>0.034</td><td>0.032</td><td>0.023</td></tr><tr><td>Linen</td><td>5</td><td>m</td><td>0.095</td><td>0.078</td><td>0.056</td><td>0.066</td><td>0.075</td><td>0.054</td></tr><tr><td>Candles</td><td>2.6</td><td>kg</td><td>0.034</td><td>0.034</td><td>0.035</td><td>0.033</td><td>0.032</td><td>0.033</td></tr><tr><td>Lamp oil</td><td>2.6</td><td>l</td><td>0.025</td><td>0.027</td><td>0.029</td><td>0.025</td><td>0.030</td><td>0.023</td></tr><tr><td>Firewood</td><td>5</td><td>Millions BTU</td><td>0.013</td><td>0.017</td><td>0.019</td><td>0.021</td><td>0.029</td><td>0.042</td></tr><tr><td>Rent</td><td>5</td><td>% of total cost</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.048</td><td>0.048</td></tr><tr><td>Total</td><td></td><td></td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr></tbody></table>

Source: Ridolfi and Nivolari (2021).
<figure><img src="image_131.png" /><figcaption>Agricultural output (1800=100)</figcaption></figure>

Source: Ridolfi and Nivolari (2021).
* Three stages for total agricultural output:

* 1280–1580: typical U shape reflecting demographic trends, Malthusian phase.

* 1600–1780: steady increase in agricultural output from 60 to 100.

* 1790–1850: further acceleration with increase of 50 percent.

* Long-run stagnation of per capita agricultural output.
* For industry and services: don't proxy with urbanization rate directly (share of population in urban centers).

* Instead, labor share in agriculture vs rest of economy combined with wage ratios.
* For industry and services: don't proxy with urbanization rate directly (share of population in urban centers).

* Instead, labor share in agriculture vs rest of economy combined with wage ratios.

* Estimate share of agricultural output $Q^{NA}/Q$:

$$ \frac{Q^A}{Q} = \frac{\pi_A L_A}{\pi L} \approx \frac{W_A L_A}{W L} $$

* $L_A/L$: labor share in agriculture (based on urbanization data).

* $\pi_A/\pi$: output per worker in agriculture vs rest of economy.

* $W_A/W$: real wage ratios in agriculture vs rest of economy.

* Assuming competitive labor markets, real wages approximate productivity.
<figure><img src="image_132.png" /><figcaption>Agricultural output share</figcaption></figure>

Source: Ridolfi and Nivolari (2021). Constant productivity gap in agriculture at 0.72 based on data from 19th century.
<figure><img src="image_133.png" /><figcaption>GDP per capita (1700=100)</figcaption></figure>

Source: Ridolfi and Nivolari (2021).
<figure><img src="image_134.png" /><figcaption>GDP per capita (1990 IGK$)</figcaption></figure>

Source: Ridolfi and Nivolari (2021).
* Three stages relative to England:

* 1250s–1400s: French GDP per capita higher with England converging.

* 1400s–1680s: similar performances, with France back to pre-Black Death levels in 1600s (due to Wars of Religion).

* Post-1680s: divergence with England growing.

* Notion of early divergence with England not confirmed by new estimates.
<figure><img src="image_135.png" /><figcaption>GDP per capita (1990 IGK$)</figcaption></figure>

Source: Ridolfi and Nivolari (2021).
* France is intermediate between South and North-West:

* No sustained growth like Holland early 16th and Britain mid 17th.

* No large fluctuations like Spain or Italy (though new estimates for Italy signal stagnation).

* France stable above the pre-Back Death level after 1660s.
China
## GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)

<figure><img src="image_136.png" /></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015).
# The Great Divergence

<figure><img src="image_137.png" /><figcaption>GDP per Capita in Leading Regions of China and Europe, 980–1850 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) based on Broadberry, Guan and Li (2018).
Broadberry et al. (2018, 2021) Solar (2021)

* Measuring China's GDP per capita is key to the Great Divergence debate.

* But no estimates for China until Broadberry et al. (2018).
Broadberry et al. (2018, 2021) Solar (2021)

* Measuring China's GDP per capita is key to the Great Divergence debate.

* But no estimates for China until Broadberry et al. (2018).

* Data scarce context:

* 960–1127: Northern Song dynasty, data for 980–1120.

* 1368–1644: Ming dynasty, data for 1400–1620.

* 1644–1911: Qing dynasty, data for 1690–1840.
• Results:

* Fluctuations at high levels during Song and Ming, downward trend during Qing.

* China richer than Britain circa 1090.

* Britain catches up by 1400.

* China richest in Asia in 1600s but behind Europe leaders.

* Great Divergence under way among areas' leaders by 1700.
* Methods:

* Patchy wage data ⇒ can't estimate agricultural output from demand side.

* Estimate agricultural output from supply side using information on output by sector as for Britain.

* For non-agriculture, both urbanization and output data available.
* Sources:

* Rich official sources: systematic, national coverage, but doubtful accuracy.

* *Shihuo zhi* (treatise on food and money) for each dynasty since 202 BC. Reports from regional authorities to central government on arable land, population, fiscal revenue, output of salt and iron.

* *Shilu* (veritable record) for Ming and Qing. Details noteworthy events in empire on daily basis.
* Sources:

* Rich official sources: systematic, national coverage, but doubtful accuracy.

* *Shihuo zhi* (treatise on food and money) for each dynasty since 202 BC. Reports from regional authorities to central government on arable land, population, fiscal revenue, output of salt and iron.

* *Shilu* (veritable record) for Ming and Qing. Details noteworthy events in empire on daily basis.

* Private historical literature, mainly contemporary historians for comprehensive examination to access empire's bureaucracy, but less systematic and complete.

* *Difang zhi* (regional gazetteers): encyclopedia of province, prefecture, county. Important sources for information on industry but sub-national level.

* Extensive secondary literature on quantitative economic history of China.
<figure><img src="image_138.png" /><figcaption>Territories of the Northern Song, Ming, and Qing Dynasties</figcaption></figure>

Source: Broadberry et al. (2018).
* Population:

* Northern Song (980–1120): official data on number of households, multiplied by family size of 5.4 (based on local gazetteers and other sources). 9 benchmark years with decadal interpolation. Wu (2000)

* Ming (1400–1644): official data on population. 5 benchmark years with decadal interpolation. Maddison (1998) based on Perkins (1969) Liu and Hwang (1979)

* Qing (1690–1840): official data on population. 3 benchmark years with decadal interpolation. Maddison (1998) based on Perkins (1969) Liu and Hwang (1979)
• Population:

* Northern Song (980–1120): official data on number of households, multiplied by family size of 5.4 (based on local gazetteers and other sources). 9 benchmark years with decadal interpolation. Wu (2000)

* Ming (1400–1644): official data on population. 5 benchmark years with decadal interpolation. Maddison (1998) based on Perkins (1969) Liu and Hwang (1979)

* Qing (1690–1840): official data on population. 3 benchmark years with decadal interpolation. Maddison (1998) based on Perkins (1969) Liu and Hwang (1979)

• Range of error: ±10% initially to ±5% in 18th century.

* Rapid growth during Song, decline during Mongol interlude, then continuous increase during Ming and Qing.
<figure><img src="image_139.png" /><figcaption>Chinese population and agriculture (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
* Agricultural output = land cultivated × crop yields per unit of land.
* Agricultural output = land cultivated × crop yields per unit of land.

* Area of cultivated land:

* Use official data, but adjusted since relate to tax units rather than arable land.

* Two potential biases:

* Tax evasion, although limited as registration needed for retaining ownership.

* Non-consistent standard measure for unit of land area (mu), which varied over regions and time.

* Data for benchmark years with decadal interpolation from many secondary sources based on official data.
* Agricultural output = land cultivated × crop yields per unit of land.

* Area of cultivated land:

* Use official data, but adjusted since relate to tax units rather than arable land.

* Two potential biases:

* Tax evasion, although limited as registration needed for retaining ownership.

* Non-consistent standard measure for unit of land area (mu), which varied over regions and time.

* Data for benchmark years with decadal interpolation from many secondary sources based on official data.

* Range of error: ±20% initially to ±10% in 18th century.

* Growth over time but did not keep up with population during Qing.
<figure><img src="image_140.png" /><figcaption>Chinese population and agriculture (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
* Crop yields:

* Northern Song (980–1120): 3 benchmark years based on 286 local observations. Wi (1985) Qi (2009)

* Ming (1400–1644): derived from land rent, with consensus that rent was about half grain output. 4 benchmark years based on 179 local observations. Gui (2000)

* Qing (1690–1840): 6 benchmark years based on 3,000 local observations. Shi (2015)

* Yields per crops averaged and weighted by distribution of cultivated land area between crops.

* Distribution of crops from official sources.
Distribution of cultivated land area by major crops

<table><thead><tr><th></th><th>1000</th><th>1400</th><th>1700</th><th>1750</th><th>1800</th><th>1850</th></tr></thead><tbody><tr><td>Rice</td><td>60.0</td><td>50.2</td><td>33.0</td><td>31.0</td><td>29.0</td><td>27.0</td></tr><tr><td>Wheat</td><td></td><td></td><td>23.0</td><td>22.0</td><td>21.0</td><td>20.0</td></tr><tr><td>Barley</td><td></td><td></td><td>7.0</td><td>7.2</td><td>7.3</td><td>7.2</td></tr><tr><td>Millet</td><td></td><td></td><td>8.0</td><td>8.2</td><td>8.4</td><td>8.2</td></tr><tr><td>Corn</td><td></td><td></td><td>0.0</td><td>1.2</td><td>2.3</td><td>3.5</td></tr><tr><td>Potatoes</td><td></td><td></td><td>0.5</td><td>0.5</td><td>0.8</td><td>1.2</td></tr><tr><td>Sorghum</td><td></td><td></td><td>8.1</td><td>8.3</td><td>8.4</td><td>8.3</td></tr><tr><td>Other crops</td><td>34.0</td><td>42.1</td><td>9.4</td><td>9.7</td><td>9.8</td><td>9.6</td></tr><tr><td>Cash crops</td><td>6.0</td><td>7.7</td><td>11.0</td><td>12.0</td><td>13.0</td><td>15.0</td></tr><tr><td>Total</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td><td>100.0</td></tr></tbody></table>

Source: Broadberry et al. (2018) based on Wu (1985), Guo (2000), and Lui (2009).
* Use 1880s data to derive remaining details. Zhang (1987)

* Agricultural inputs set to 15% of gross output, based on ratio from 1880s data.

* Net output of livestock and fishing set at 10% of net output of grain crops, based on ratio from 1880s data.

* Net output of cash crops (sugarcane, cotton, tobacco, peanuts, hemp) set to 25% of net output of grain crops, based on ratio from 1880s data.
* Use 1880s data to derive remaining details. Zhang (1987)

* Agricultural inputs set to 15% of gross output, based on ratio from 1880s data.

* Net output of livestock and fishing set at 10% of net output of grain crops, based on ratio from 1880s data.

* Net output of cash crops (sugarcane, cotton, tobacco, peanuts, hemp) set to 25% of net output of grain crops, based on ratio from 1880s data.

* Rising yields over time, with introduction of higher-yield rice type. But insufficient to compensate for decline in cultivated land per capita.
<figure><img src="image_141.png" /><figcaption>Chinese population and agriculture (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
<figure><img src="image_142.png" /><figcaption>Chinese population and agriculture (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
* Industrial output across 4 main sectors:

* Metals and mining.

* Food processing.

* Textiles and other manufacturing.

* Building.

* Set of benchmark years and decadal interpolation.

* Get output from each sector and aggregate into index using value-added weights from benchmark year of 1880. Zhang (1987)
* Metals and mining: iron, copper, salt. Liu (1993), Huang (1989), Wang (1995, 2005), Guo (1997), Zeyi (1962), Li (1979), Xu and Wu (1985).

* Northern Song (980–1120): iron used for coining and government purchases, copper used for minting, salt tax data.

* Ming (1400–1644): state-run iron and copper industry, private iron and copper tax revenues, salt consumption per head multiplied by population.

* Qing (1690–1840): iron output from Guangdong industrial center and other provinces, copper output for Yunnan industrial center and other provinces.
* Metals and mining: iron, copper, salt. Liu (1993), Huang (1989), Wang (1995, 2005), Guo (1997), Zeyi (1962), Li (1979), Xu and Wu (1985).

* Northern Song (980–1120): iron used for coining and government purchases, copper used for minting, salt tax data.

* Ming (1400–1644): state-run iron and copper industry, private iron and copper tax revenues, salt consumption per head multiplied by population.

* Qing (1690–1840): iron output from Guangdong industrial center and other provinces, copper output for Yunnan industrial center and other provinces.

* Volatility with a boom during Song, decline during Ming (decline in weapons production and minting of coins), and rebound during Qing (end of state monopolies).
<figure><img src="image_143.png" /><figcaption>Chinese industry (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
* Food processing: assumed to grow following agricultural output (as for England).

* Building: assumed to grow following population and urbanization (Great Wall completed before 1400). Rozman (1973) Maddison (1998)

* Textile: assumed to grow following population.
<figure><img src="image_144.png" /><figcaption>Chinese industry (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
* Industry output index:

* Convert output into values using prices and deflate.

* Aggregate using added-value weights of each sector from benchmark year 1880. Zhang (1987)
* Industry output index:

* Convert output into values using prices and deflate.

* Aggregate using added-value weights of each sector from benchmark year 1880. Zhang (1987)

* Industrial output grows at same rate as population (also, partly based on population trends).

* 1400s “boom” due to metals and mining.
<figure><img src="image_145.png" /><figcaption>Chinese industry (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
* Service sector output:

* Commerce: volume of agricultural and industrial goods distributed based on 1840 weights (58% and 42%) and commercialization rates (17% for agriculture and 100% for industry). Wu (1998)

* Government: derived from number and civil servants and soldiers multiplied by salaries, deflated with GDP deflator. Li (1998) Wang (1995) Chen (2008) Shi and Xu (2008)

* Housing and domestic services: assumed to follow population and urbanization, as building industry. Rozman (1973)
* Service sector output:

* Commerce: volume of agricultural and industrial goods distributed based on 1840 weights (58% and 42%) and commercialization rates (17% for agriculture and 100% for industry). Wu (1998)

* Government: derived from number and civil servants and soldiers multiplied by salaries, deflated with GDP deflator. Li (1998) Wang (1995) Chen (2008) Shi and Xu (2008)

* Housing and domestic services: assumed to follow population and urbanization, as building industry. Rozman (1973)

* Sharp rise in size of government sector during Song, maintained during Ming, decline during Qing.

* Convert output into values using prices.

* Aggregate using value-added weights of each sector in benchmark year 1880. Zhang (1987)
<figure><img src="image_146.png" /><figcaption>Chinese services (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
<figure><img src="image_147.png" /><figcaption>Chinese services (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
Chinese agricultural, industrial, and service sector output (1840=100, log scale)

<figure><img src="image_148.png" /></figure>

Source: Broadberry et al. (2018).
* Combine output indices to produce real GDP.

* Need value added weights of each sector ⇒ use benchmark year of 1880. Zhang (1987)

* Take absolute level of GDP in 1840 to calculate nominal value of GDP of each sector, assuming stability of output shares between 1840 and 1880.

* To calculate GDP in real terms, use a general price index to construct a GDP deflator based on grain and cloth price index weighted 67% and 33% (based on output shares in 1840).
<figure><img src="image_149.png" /><figcaption>Chinese constant price GDP and GDP per capita (1840=100, log scale)</figcaption></figure>

Source: Broadberry et al. (2018).
Data reliability grades

<table><thead><tr><td>Reliability Grade</td><td>Margin of Error (in Percent)</td><td>Average Margin of Error (in Percent)</td></tr></thead><tbody><tr><td>A. Firm figures</td><td>± less than 5</td><td>± 2.5</td></tr><tr><td>B. Good figures</td><td>± 5 to 15</td><td>± 10</td></tr><tr><td>C. Rough estimates</td><td>± 15 to 25</td><td>± 20</td></tr><tr><td>D. Conjectures</td><td>± more than 25</td><td>± 40</td></tr></tbody></table>

Source: Broadberry et al. (2018).
Reliability assessments for Chinese data

<table><thead><tr><th></th><th>Northern Song</th><th>Ming</th><th>Qing</th></tr></thead><tbody><tr><td><em>Agriculture</em></td><td></td><td></td><td></td></tr><tr><td>Cultivated land</td><td>C</td><td>B</td><td>A</td></tr><tr><td>Crop yields</td><td>B</td><td>B</td><td>A</td></tr><tr><td><em>Industry</em></td><td></td><td></td><td></td></tr><tr><td>Iron</td><td>B</td><td>C</td><td>B</td></tr><tr><td>Copper</td><td>B</td><td>C</td><td>B</td></tr><tr><td>Salt</td><td>B</td><td>B</td><td>B</td></tr><tr><td>Food processing</td><td>C</td><td>B</td><td>A</td></tr><tr><td>Textile</td><td>B</td><td>B</td><td>A</td></tr><tr><td>Building</td><td>B</td><td>B</td><td>A</td></tr><tr><td><em>Services</em></td><td></td><td></td><td></td></tr><tr><td>Commerce</td><td>B</td><td>B</td><td>A</td></tr><tr><td>Government</td><td>B</td><td>B</td><td>B</td></tr><tr><td>Housing and domestic service</td><td>B</td><td>B</td><td>A</td></tr><tr><td><em>Real aggregates</em></td><td></td><td></td><td></td></tr><tr><td>GDP</td><td>B</td><td>B</td><td>A</td></tr><tr><td>Population</td><td>B</td><td>B</td><td>A</td></tr><tr><td>GDP per capita</td><td>B</td><td>B</td><td>A</td></tr><tr><td><em>Nominal aggregates</em></td><td></td><td></td><td></td></tr><tr><td>GDP deflator</td><td>D</td><td>C</td><td>B</td></tr><tr><td>Nominal GDP</td><td>C</td><td>C</td><td>B</td></tr></tbody></table>

Source: Broadberry et al. (2018).
Sensitivity analysis for Chinese GDP

<table><thead><tr><th></th><th>Northern Song<br>(in Percent)</th><th>Ming<br>(in Percent)</th><th>Qing<br>(in Percent)</th></tr></thead><tbody><tr><td>Cultivated land</td><td>± 13.7</td><td>± 5.2</td><td>± 1.6</td></tr><tr><td>Crop yields</td><td>± 6.8</td><td>± 5.2</td><td>± 1.6</td></tr><tr><td>Iron</td><td>± 0.02</td><td>± 0.04</td><td>± 0.03</td></tr><tr><td>Copper</td><td>± 0.0039</td><td>± 0.0001</td><td>± 0.0016</td></tr><tr><td>Salt</td><td>± 0.04</td><td>± 0.07</td><td>± 0.13</td></tr><tr><td>Food processing</td><td>± 0.12</td><td>± 0.05</td><td>± 0.01</td></tr><tr><td>Textiles</td><td>± 0.29</td><td>± 0.29</td><td>± 0.09</td></tr><tr><td>Building</td><td>± 0.07</td><td>± 0.09</td><td>± 0.03</td></tr><tr><td>Commerce</td><td>± 0.80</td><td>± 0.68</td><td>± 0.22</td></tr><tr><td>Government</td><td>± 1.20</td><td>± 2.73</td><td>± 0.85</td></tr><tr><td>Housing and domestic service</td><td>± 0.67</td><td>± 0.84</td><td>± 0.25</td></tr></tbody></table>

Source: Broadberry et al. (2018).
* Comparisons across dynasties:

* Territorial expansion with limited effects, since sparsely populated.

* GDP expansion in all three dynasties:

* Northern Song (980–1120): 0.87% growth per year.

* Ming (1400–1644): 0.27% growth per year.

* Qing (1690–1840): 0.36% growth per year.

* But GDP per capita stagnant at high level, with downward trend during Qing (−0.34% per year).

* Same GDP per capita in 1620 and 980, but 70% of 980 level in 1840.
<figure><img src="image_150.png" /><figcaption>Territories of the Northern Song, Ming, and Qing Dynasties</figcaption></figure>

Source: Broadberry et al. (2018).
* Early developments with high levels of agricultural productivity, but stuck in high-level equilibrium.

* Decline during Qing due to rapid population expansion leading to division of land holdings without productivity growth, not compensated with expansion of industrial and service sector output.

* One hypothesis is sclerosis due to oversized bureaucracy.
* Comparing China and Britain:

* Need to compare in common currency: 1990 international $.

* Use 1840 benchmark year for which enough price data available.

* Also use currency exchange rates and basket weights to construct PPP values.

* In PPP, China GDP per capita is 24% of Britain in 1840.

* Converting to international $ ⇒ $600 vs $2,500 in 1840.
China vs Britain PPP for 1840

<table><thead><tr><th></th><th>China<br>Tael/lb</th><th>GB<br>£ per lb</th><th>PPP<br>Tael per £</th><th>Chinese<br>Weights</th><th>British<br>Weights</th></tr></thead><tbody><tr><td>Rice</td><td>0.01407</td><td>0.02500</td><td>0.56</td><td>0.201</td><td>0.000</td></tr><tr><td>Wheat</td><td>0.00900</td><td>0.00691</td><td>1.30</td><td>0.134</td><td>0.335</td></tr><tr><td>Sugar</td><td>0.04900</td><td>0.02191</td><td>2.24</td><td>0.134</td><td>0.134</td></tr><tr><td>Tea</td><td>0.09347</td><td>0.13021</td><td>0.72</td><td>0.134</td><td>0.134</td></tr><tr><td>Salt</td><td>0.00544</td><td>0.00134</td><td>4.07</td><td>0.067</td><td>0.067</td></tr><tr><td>Iron</td><td>0.04195</td><td>0.00402</td><td>10.44</td><td>0.046</td><td>0.046</td></tr><tr><td>Cotton cloth</td><td>0.20690</td><td>0.11301</td><td>1.83</td><td>0.284</td><td>0.284</td></tr><tr><td>FOOD</td><td></td><td></td><td>1.53</td><td></td><td></td></tr><tr><td>OTHER</td><td></td><td></td><td>3.04</td><td></td><td></td></tr><tr><td>TOTAL</td><td></td><td></td><td>2.03</td><td></td><td></td></tr></tbody></table>

Source: Broadberry et al. (2018).
Benchmark estimate of China vs Britain GDP per capita in 1840

<table><tbody><tr><td colspan="2"><em>China</em></td></tr><tr><td>Nominal GDP (million tael)</td><td>5,379</td></tr><tr><td>Population (million)</td><td>412</td></tr><tr><td>GDP per capita (tael)</td><td>13.05</td></tr><tr><td colspan="2"><em>England</em></td></tr><tr><td>Nominal GDP (£ million)</td><td>496.30</td></tr><tr><td>Population (million)</td><td>18.332</td></tr><tr><td>GDP per capita (£)</td><td>27.07</td></tr><tr><td colspan="2"><em>Exchange rates</em></td></tr><tr><td>Silver exchange rate (tael per £)</td><td>3.20</td></tr><tr><td>PPP (tael per £)</td><td>2.03</td></tr><tr><td colspan="2"><em>Comparative China/GB GDP per capita (percent)</em></td></tr><tr><td>At silver exchange rate</td><td>15.04</td></tr><tr><td>At PPP</td><td>23.76</td></tr><tr><td colspan="2"><em>GDP in 1990 international dollars</em></td></tr><tr><td>GB</td><td>2,521</td></tr><tr><td>China</td><td>599</td></tr></tbody></table>

Source: Broadberry et al. (2018).
GDP per capita levels in China and Britain in 1990 IGK$

<table><thead><tr><th></th><th>China ($1990)</th><th>GB ($1990)</th><th>China/GB (GB = 100)</th></tr></thead><tbody><tr><td>980</td><td>840</td><td></td><td></td></tr><tr><td>1020</td><td>997</td><td></td><td></td></tr><tr><td>1060</td><td>962</td><td></td><td></td></tr><tr><td>1090</td><td>862</td><td>723</td><td>119.2</td></tr><tr><td>1120</td><td>833</td><td></td><td></td></tr><tr><td>1270</td><td></td><td>728</td><td></td></tr><tr><td>1300</td><td></td><td>724</td><td></td></tr><tr><td>1400</td><td>991</td><td>1,045</td><td>94.8</td></tr><tr><td>1450</td><td>970</td><td>1,011</td><td>95.9</td></tr><tr><td>1500</td><td>852</td><td>1,068</td><td>79.8</td></tr><tr><td>1570</td><td>873</td><td>1,096</td><td>79.7</td></tr><tr><td>1600</td><td>859</td><td>1,077</td><td>79.8</td></tr><tr><td>1650</td><td></td><td>1,055</td><td></td></tr><tr><td>1700</td><td>1,089</td><td>1,563</td><td>69.7</td></tr><tr><td>1750</td><td>749</td><td>1,710</td><td>43.8</td></tr><tr><td>1800</td><td>654</td><td>2,080</td><td>31.4</td></tr><tr><td>1840</td><td>599</td><td>2,521</td><td>23.8</td></tr><tr><td>1850</td><td>600</td><td>2,997</td><td>20.0</td></tr></tbody></table>

Source: Broadberry et al. (2018).
GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)

<figure><img src="image_151.png" /></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015).
* Comparison with Europe:

* China richest country in the world during Northern Song (980–1120).

* Italy ahead of China by 1300, probably earlier.

* Britain catches up with China by 1400.

* Netherlands, Britain, Italy substantially above China by 1500.
* Comparison with Europe leading regions:

* But compare large economy with smaller ones.

* More meaningful to use richer region of China: Yangzi Delta region.

* In benchmark year 1820, Yangzi Delta 54% of Netherlands. Bozhong and van Zanden (2012)

⇒ Yangzi Delta region is $1,050 so 75% higher than China whole.
* Comparison with Europe leading regions:

* But compare large economy with smaller ones.

* More meaningful to use richer region of China: Yangzi Delta region.

* In benchmark year 1820, Yangzi Delta 54% of Netherlands. Bozhong and van Zanden (2012)

⇒ Yangzi Delta region is $1,050 so 75% higher than China whole.

* Apply this ratio to China to get Yangzi series (high hypothesis).

* Compare to Europe leader.

* Build lower series based on alternative estimates in 1850, based on retroprojecting Maddison's figure for 1933. Xu et al. (2017)
GDP per capita lin leading regions of Europe and China in 1990 IGK$

<figure><img src="image_152.png" /></figure>

Source: Broadberry et al. (2018).
Solar (2021)

* Issues with Broadberry et al.'s (2018) estimates of government size.

* Government spending per capita 7× higher in early Ming than in 19th century.

* Government share is 35% is early Ming and 15% in late Song to early Qing.

* In Europe, never above 10%.

* Historical evidence on China points to a sector never above 10%.
<figure><img src="image_153.png" /><figcaption>Government per capita and as share of GDP</figcaption></figure>

Source: Solar (2021).
* Estimating the government sector:

* Number employed in civil service and military, multiplied by salaries, deflated within index based on grain and cloth prices.

* But army in Ming is 1.3–1.8M with population of 70M so male labor force of 14M.

* Soldier's wages close to day laborers.

* So government share less than 10–15%.
* Double accounting by Broadberry et al. (2018)?

* Much of soldiers' time spent growing food, transporting grain.

* Military land accounted for 3-5% of all cultivated land.

* Need to net this out from government sector since already in agricultural output.
* Double accounting by Broadberry et al. (2018)?

* Much of soldiers' time spent growing food, transporting grain.

* Military land accounted for 3-5% of all cultivated land.

* Need to net this out from government sector since already in agricultural output.

* Also, many civil servants participate in corvée labor for handicraft production, so need to net this out since already in industrial output.
* Three alternative estimates of China's government sector:

* Assume grew in line with population.

* Assume constant share of GDP based on benchmark year of 1840.

* Assume GDP share of alternative source (Song: 13%; Ming: 7%; Qing: 6%). Feuerwerker (1984)
* Three alternative estimates of China's government sector:

* Assume grew in line with population.

* Assume constant share of GDP based on benchmark year of 1840.

* Assume GDP share of alternative source (Song: 13%; Ming: 7%; Qing: 6%). Feuerwerker (1984)

* Song and Qing somewhat unchanged, but Ming largely overestimated (25% lower).
<figure><img src="image_154.png" /><figcaption>GDP per capita: original and revised government sector</figcaption></figure>

Source: Solar (2021).
* Other issue: Broadberry et al.'s (2018) benchmark year for nominal GDP:

* Use 1840 benchmark year for GDP level, using 1880 GDP for value-added shares.

* More direct estimate of GDP for 1850. Xu et al. (2015, 2017) Ma and de Jong (2019)

* Prefer benchmark of $532 in 1850 instead of $599 in 1840.

* Revised estimates show China much behind by 1400.

* Even looking at leading regions, Great Divergence might have occurred as early as 1400s.
GDP per capita levels in Europe and Asia (1990 IGK$)

<table><thead><tr><th></th><th>China</th><th>Italy</th><th>Netherlands</th><th>Great Britain</th></tr></thead><tbody><tr><td>1020</td><td>902</td><td></td><td></td><td></td></tr><tr><td>1090</td><td>733</td><td></td><td></td><td></td></tr><tr><td>1300</td><td></td><td>1466</td><td></td><td>723</td></tr><tr><td>1400</td><td>600</td><td>1570</td><td>958</td><td>724</td></tr><tr><td>1500</td><td>637</td><td>1408</td><td>1141</td><td>1045</td></tr><tr><td>1600</td><td>632</td><td>1224</td><td>1825</td><td>1068</td></tr><tr><td>1700</td><td>874</td><td>1344</td><td>1849</td><td>1077</td></tr><tr><td>1800</td><td>576</td><td>1327</td><td>1974</td><td>1563</td></tr><tr><td>1850</td><td>535</td><td>1306</td><td>2397</td><td>2080</td></tr><tr><td></td><td></td><td></td><td></td><td>2997</td></tr></tbody></table>

Source: Solar (2021).
GDP per capita in leading regions of Europe and China in 1990 IGK$

<figure><img src="image_155.png" /></figure>

Source: Broadberry et al. (2018).
<figure><img src="image_156.png" /><figcaption>GDP per capita in leading regions of Europe and China in 1990 IGK$</figcaption></figure>

Source: Solar (2021).
Broadberry et al. (2021)

* Response of Broadberry et al. (2021):

* Broadly agree on government sector and revise their estimates.

* Disagree with revision of weight in benchmark year of 1840.

* Keep their original weighting scheme, decreasing a little the size of government based on auxiliary literature.

<figure><img src="image_157.png" /></figure>
Broadberry et al. (2021)

* Response of Broadberry et al. (2021):

* Broadly agree on government sector and revise their estimates.

* Disagree with revision of weight in benchmark year of 1840.

* Keep their original weighting scheme, decreasing a little the size of government based on auxiliary literature.

⇒ Keep interpretation that Great Divergence is circa 1700.

* The debate continues, further issues pointed out in Solar (2021, WP).
<figure><img src="image_158.png" /><figcaption>Real government services in China (1840=100)</figcaption></figure>

Source: Broadberry et al. (2021).
Revised sectoral shares of GDP at constant 1840 prices (%)

<table><thead><tr><th></th><th>Agriculture</th><th>Industry</th><th>Services<br>(Including Government)</th><th>Government</th></tr></thead><tbody><tr><td>980</td><td>70.1</td><td>5.5</td><td>24.4</td><td>4.8</td></tr><tr><td>1120</td><td>71.1</td><td>5.8</td><td>23.0</td><td>2.4</td></tr><tr><td>1400</td><td>65.7</td><td>5.8</td><td>28.5</td><td>8.6</td></tr><tr><td>1620</td><td>69.7</td><td>5.6</td><td>24.7</td><td>4.7</td></tr><tr><td>1690</td><td>67.1</td><td>5.7</td><td>27.2</td><td>7.4</td></tr><tr><td>1840</td><td>66.1</td><td>8.1</td><td>25.8</td><td>2.1</td></tr></tbody></table>

Source: Broadberry et al. (2021).
Real GDP per capita in China in 1990 IGK$

<figure><img src="image_159.png" /></figure>

Source: Broadberry et al. (2021).
GDP per capita in leading regions of Europe and China in 1990 IGK$

<figure><img src="image_160.png" /></figure>

Source: Broadberry et al. (2018).
Japan
## GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)

<figure><img src="image_161.png" /></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015).
Bassino et al. (2019)

* Interesting features of the Japanese case:

* Some historians argued that Japan was on par with Europe until the 19th century. Hanley (1983, 1986)

* First Asian country to catch up Europe in the 20th century after the Meiji Restoration of 1868, while China and India fell behind.

* No "new generation" estimates for Japan until Bassino et al. (2019) for 710-1874.

* Broadberry's series relies on Bassino et al.'s working paper.
* Data scarcity ⇌ only benchmark years, no annual time series.

* Ancient period (710–1192): 730, 950, 1150.

* Medieval period (1192–1600): 1280, 1450.

* Tokugawa period (1600–1868): 1600, 1721, 1804, 1846

* Meiji period (1868–1912): 1874.
* Methods:

* Agriculture:

* From supply side: land use and crops harvested × crop yields.

* Combined with indirect demand approach based on population, wages, and prices.

* Outside agriculture: indirect approach with urbanization proxies.
* Results:

* Ancient period (710–1192): Malthusian fluctuations, income per capita and population moving in opposite directions.

* Medieval period (1192–1600): positive growth trend in 1450–1600 (uncertain).

* Tokugawa period (1600–1868): positive growth trend in 1721–1868 but less than in Europe.

* Meiji period (1868–1912): take-off and modern economic growth.
* Population:

* Ancient period (710–1192):

* 730: number and average size of administrative villages (assuming 1,250–400 inhabitants) and adding 10% of slaves and 150k urban dwellers. Sawada (1927), Kamata (1984), Farris (2009)

* 950: land area combined with land requirement per person, adding 15% of infants, 150k urban dwellers, 500k slaves. Alternative: number of administrative villages. Yokoyama (1879), Kito (1983), Farris (2009)

* 1150: same method as for 950.
* Population:

* Ancient period (710–1192):

* 730: number and average size of administrative villages (assuming 1,250–400 inhabitants) and adding 10% of slaves and 150k urban dwellers. Sawada (1927), Kamata (1984), Farris (2009)

* 950: land area combined with land requirement per person, adding 15% of infants, 150k urban dwellers, 500k slaves. Alternative: number of administrative villages. Yokoyama (1879), Kito (1983), Farris (2009)

* 1150: same method as for 950.

* Medieval period (1192–1600):

* 1280: same method based on sample of land registers. Farris (2006)

* 1450: number of soldiers and applying a ratio for rural areas, and allowing for additional urban dwellers. Farris (2006)