# TSE M2 PPD Institutions and Long-Run Development
(2022-2023)

Victor Gay

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## To cite this version:

Victor Gay. TSE M2 PPD Institutions and Long-Run Development (2022-2023). Master. Institutions and Long-Run Development, France. 2023, pp.716. hal-03154292v2

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Institutions and Long-Run Development

Victor Gay

University of Toulouse 1 Capitole

Toulouse School of Economics

Institute for Advanced Study in Toulouse

M2 PPD, Semester 2, 2023

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<figure><img src="image_4.png" /></figure>

IAST

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Toulouse
School of
Economics
Topic 1
Introduction
Why are some countries so much richer than others?
① Cross-country differences in income.

② GDP: definition, measurement, issues.

③ Economic growth and income differences.

④ Origins of current income differences.

⑤ Neoclassical growth theory: the Solow growth model.

⑥ Correlates of economic growth.

⑦ Fundamental causes of long-run growth.
Current cross-country differences in income
## 2017 PPP-adjusted GDP in bil. 2011 US $

<figure><img src="image_6.png" /></figure>
2017 PPP-adjusted GDP per Capita in thousands. 2011 US $

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>Distribution of Countries: GDP per Capita</figcaption></figure>

Source: Penn World Table 9.1, 110 countries.
<figure><img src="image_9.png" /><figcaption>Distribution of Countries: Log GDP per Capita</figcaption></figure>

Source: Penn World Table 9.1, 110 countries.
Population-Weighted Distribution of Countries: Log GDP per Capita

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

Source: Penn World Table 9.1, 110 countries.
<figure><img src="image_11.png" /><figcaption>Distribution of Countries: GDP per Capita</figcaption></figure>

Source: Maddison Project Database 2018, 86 countries.
<figure><img src="image_12.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: definition and measurement
* Definition

* A distorted picture

* Income and welfare

* Measuring historical GDP
## 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_13.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_14.png" /></figure>
<figure><img src="image_15.png" /><figcaption>Welfare differs from income by about 35%</figcaption></figure>
de Jong and Palma (2018) Broadberry (2021)

* Historical national accounting

* Comparisons over time and space
* Potential value of growth accounting in long-run development:

* 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 long-run development:

* Highly sensitive to methodology and data quality.

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

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

* Early Modern Probate Inventories Database: inventories of Church for same information. Overton (1991, 2000) Overton et al. (2004)

* Modern Farm Accounts Database: sample of farmers accounts. Turner et al. (2001)
* Three databases across different periods:

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

* Early Modern Probate Inventories Database: inventories of Church for same information. Overton (1991, 2000) Overton et al. (2004)

* Modern Farm Accounts Database: sample of farmers accounts. Turner et al. (2001)

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

* Yields: trends from microdata in databases.

* Total output: apply to total acreage of country.
* Three databases across different periods:

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

* Early Modern Probate Inventories Database: inventories of Church for same information. Overton (1991, 2000) Overton et al. (2004)

* Modern Farm Accounts Database: sample of farmers accounts. Turner et al. (2001)

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

* Yields: trends from microdata in databases.

* Total output: apply to total acreage of country.

* Convert output into current prices, weights for real output index.
* Many databases for the main industrial sectors up to 1700:

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

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

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

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

* Construction: cathedral building, housebuilding (population).

* Book production: titles listed by British Library.
* Many databases for the main industrial sectors up to 1700:

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

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

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

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

* Construction: cathedral building, housebuilding (population).

* Book production: titles listed by British Library.

* Use series to build industrial production index.
* Method from Deane and Cole (1962).

* Broken down into subsectors:

* Commerce: trade, freight transport, financial services.

* Housing and domestic services: rate of population.

* Government: revenues. O'Brien and Hunt (1999)
## Putting it together:

* Combine output series with sectoral weights based on input-output tables for 1841. 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 it together:

* Combine output series with sectoral weights based on input-output tables for 1841. 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).
* 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:

* $\alpha_1 = -0.6$.

* $\alpha_2 = 0.1$.

* $\Rightarrow \beta = 0.5$.
* 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.
## Blum and Clovin (2018) chap 46

* Within countries:

* Deflation issues: changing baskets of goods over time.

* Heterogeneous sources: systematic and changing biases.
## Blum and Clovin (2018) chap 46

* 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.
## Blum and Clovin (2018) chap 46

* 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.
Economic growth and income differences
* Growth rates explain large income differences.
* 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.
* 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_16.png" /><figcaption>Distribution of GDP per Capita Growth Rates</figcaption></figure>

Source: Penn World Table 9.1
<figure><img src="image_17.png" /><figcaption>Evolution of Income per Capita across Countries</figcaption></figure>

Source: Maddison Project Database 2018.
<figure><img src="image_18.png" /><figcaption>Evolution of Income per Capita across Continents</figcaption></figure>

Source: Maddison Project Database 2018.
Long-run growth: the Great and the Little Divergences
Broadberry (2021) Court (2020)

* Europe's Little Divergence

* Asia's Little Divergence

* The Great Divergence

⇒ New estimates enable revise past views from e.g., Maddison (2001)
<figure><img src="image_19.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) 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_20.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) 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_21.png" /><figcaption>GDP per Capita in Europe, 1270–1870 (1990 GKI$, Log scale)</figcaption></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).
* First turning point: Black Death (1348).

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

* After: GB and NL permanently faster (and richer).

* 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).

* 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).

* 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_22.png" /><figcaption>GDP per Capita in Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015), Broadberry, Custodis and Gupta (2015)
# Asia's Little Divergence

## 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.
# Asia's Little Divergence

## 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
## GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)

<figure><img src="image_23.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_24.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).
# The Great Divergence

* 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 13th–14th?
* Year 1000:

* Leadership of China at $1,000.

* Italy ≈ $ 911; Britain ≈ $ 723.

* Did Great Divergence start 13th–14th?

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

* Compare leading regions (Yangzi Delta vs Italy, NL, UK)
* Year 1000:

* Leadership of China at $1,000.

* Italy ≈ $ 911; Britain ≈ $ 723.

* Did Great Divergence start 13th–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
# The Great Divergence

* Important questions:

* Was China ever really wealthy?

* When did China fall behind?

* Was it the result of positive growth in Europe or negative growth in China?

* Estimates for China 980–1840 by Broadberry et al. (2018):

* Britain caught up with China as a whole by 1400.

* The Great Divergence with the richest parts of China circa 1700.

* Due to both rising incomes in Europe and falling incomes in China.
* Issues in Broadberry et al. (2018) pointed out by Solar (2021):

* Constructed based on only 2 series: grain output and population.

* Government sector share too high (e.g., 30% of GDP in 1400), requiring downward correction for Ming (1368–1644)

* Benchmark to anchor series in 1840 was too high.

* China leader estimated with constant upward adjustment of 75% based on 1820s benchmark when textile booming there (perhaps relative income different in other periods).
## Estimates of China's GDP per capita, 980–1910

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

Source: Solar (2021) based on Broadberry et al. (2018, 2021), Ma and de Jong (2019), and Xu et al. (2017).
# The Great Divergence

* Alternative: compare Western Europe to China.

* Use series from Britain, Holland, France, Spain, Italy, Germany (80% of Europe population).

* With revised estimates, from 1500 on, Europe always richer than China.

* Perhaps divergence in trends from 1650s.

* High degree of uncertainty remains.
<figure><img src="image_26.png" /><figcaption>GDP per capita in “Europe” and China, 1500–1850</figcaption></figure>

Source: Solar (2021) based on Palma and Reis (2019), Ridolfi and Nuvolari (2021), de Vries (1994), Malanima (2010), Broadberry et al. (2018, 2021), Ma and de Jong (2019), and Xu et al. (2017).
* Why are some countries so much richer than others?

* Why do some countries grow so much faster?

* What sustains growth over the long run?
Neoclassical growth theory
⇒ Explanation: differences in preferences and endowments.

* Traditional growth theory (50s, 60s): Sollow and Cass-Koopmans.

* Paths of factor accumulation.

* Differences in saving rates (Sollow) or preferences (Cass-Koopmans).

* Modern growth theory (80s, 90s): Lucas and Romer.

* Externalities from physical and human capital.

* Endogenous steady-state growth and technical progress.
* Based on Solow (1956).

* Micro foundations: neoclassical aggregate production function.

* Dynamic general equilibrium model.

* Simple and abstract representation of complex reality.
* Closed economy, unique final good, infinite horizon.

* Many identical households, saving $s \in [0, 1]$ of disposable income.

* Many identical firms with representative production function:

$$Y = F(K, L, A) \quad \text{e.g.} \quad Y = AK^{\alpha}L^{1-\alpha}, \alpha \in (0, 1)$$

* $Y$: production. $K$: capital. $L$: labor. $A$: technology.

* $F_K, F_L > 0, F_{KK}, F_{LL} < 0$. Decreasing returns to scale.

* $A$ is free: nonexcludable and nonrival.
* Competitive markets:

* Households and firms are price-takers.

* Prices clear markets.

* Private ownership of endowments.

* Firms rent labor at rate $w$ and capital at rate $R$.

* Market clearing conditions (demand = supply): $L = \bar{L}$ and $K = \bar{K}$.

* Capital depreciates at rate $\delta \in (0, 1)$.
* Firms solve this optimization problem:

$$ \max_{K,L} F(K, L, A) - RK - wL $$
* Firms solve this optimization problem:

$$ \max_{K,L} F(K, L, A) - RK - wL $$

* In equilibrium:

* Markets clear.

* Marginal products equal marginal costs: $w = F_L$ and $R = F_K$.

* Firms make zero profits: $Y = wL + RK$
* Because of depreciation: $K(t + 1) = (1 - \delta)K(t) + I(t)$.
* Because of depreciation: $K(t + 1) = (1 - \delta)K(t) + I(t)$.

* National accounting: $Y(t) = C(t) + I(t)$.
* Because of depreciation: $K(t + 1) = (1 - \delta)K(t) + I(t)$.

* National accounting: $Y(t) = C(t) + I(t)$.

* By substitution: $K(t + 1) = Y(t) + (1 - \delta)K(t) - C(t)$.
* Because of depreciation: $K(t + 1) = (1 - \delta)K(t) + I(t)$.

* National accounting: $Y(t) = C(t) + I(t)$.

* By substitution: $K(t + 1) = Y(t) + (1 - \delta)K(t) - C(t)$.

* Closed economy: $S(t) = I(t) = Y(t) - C(t)$.
* Because of depreciation: $K(t + 1) = (1 - \delta)K(t) + I(t)$.

* National accounting: $Y(t) = C(t) + I(t)$.

* By substitution: $K(t + 1) = Y(t) + (1 - \delta)K(t) - C(t)$.

* Closed economy: $S(t) = I(t) = Y(t) - C(t)$.

* Household save a fraction of income: $S(t) = sY(t)$.
* Because of depreciation: $K(t + 1) = (1 - \delta)K(t) + I(t)$.

* National accounting: $Y(t) = C(t) + I(t)$.

* By substitution: $K(t + 1) = Y(t) + (1 - \delta)K(t) - C(t)$.

* Closed economy: $S(t) = I(t) = Y(t) - C(t)$.

* Household save a fraction of income: $S(t) = sY(t)$.

* Fundamental law of motion of the Solow growth model:

$$K(t + 1) = sF(K(t), L(t), A(t)) + (1 - \delta)K(t)$$
## Definition

An equilibrium is a sequence of allocations and prices such that:

$$ \{K(t), Y(t), C(t), w(t), R(t)\}_{t=0}^{\infty} $$

* Markets clear.

* Marginal products equal marginal costs.

* The law of motion of capital holds.
• No population growth: $L(t) = L$.

• No technological progress: $A(t) = A$.

• Define capital-labor ratio: $k(t) = K(t)/L$.
* No population growth: $L(t) = L$.

* No technological progress: $A(t) = A$.

* Define capital-labor ratio: $k(t) = K(t)/L$.

* Output (income) per capita is:

$$y(t) = \frac{Y(t)}{L} = F\left(\frac{K(t)}{L}, 1, A\right) = f(k(t))$$
* No population growth: $L(t) = L$.

* No technological progress: $A(t) = A$.

* Define capital-labor ratio: $k(t) = K(t)/L$.

* Output (income) per capita is:

$$y(t) = \frac{Y(t)}{L} = F\left(\frac{K(t)}{L}, 1, A\right) = f(k(t))$$

* Law of motion becomes: $k(t + 1) = sf(k(t)) + (1 - \delta)k(t)$.
## Definition

A steady-state equilibrium without technological progress and population growth is an equilibrium path in which

$$k(t) = k^*, \quad \forall t$$
## Definition

A steady-state equilibrium without technological progress and population growth is an equilibrium path in which

$$k(t) = k^*, \quad \forall t$$

* With the law of motion: $sf(k^*) = \delta k^*$.

* In equilibrium, investment per capita replenishes depreciated capital.
<figure><img src="image_27.png" /></figure>
<figure><img src="image_28.png" /></figure>
* Once steady-state is reached, no growth.

* Additional growth achieved by shifting steady-state:

* More investment: $↑s$.

* Improved technology: $↑A$ (exogenous).

* Larger population: $↑L$ (exogenous).

* Straightforward to include human capital.

* Source of growth: physical and human capital accumulation.
Correlates of economic growth
<figure><img src="image_29.png" /><figcaption>Average GDP per Capita Growth and Capital Investment, 1960–2017</figcaption></figure>

Source: Penn World Table 9.1, 111 countries.
<figure><img src="image_30.png" /><figcaption>Average GDP per Capita Growth and Human Capital, 1960–2017</figcaption></figure>

Source: Maddison Project Database 2018, Barro–Lee Dataset, 100 countries.
<figure><img src="image_31.png" /><figcaption>Average GDP per Capita Growth and TFP, 1960–2017</figcaption></figure>

Source: Penn World Table 9.1, 71 countries.
* Best cross-country Educational Attainment data source: Barro-Lee.

* Reference paper: Barro and Lee (2013).

* Educational attainment measures by sex and age.

* 146 countries, 1950–2010, 5-year intervals.
* Factors accumulation are *proximate causes* of economic growth.

"*The factors we have listed (innovation, economies of scale, education, capital accumulation, etc.) are not causes of growth; they are growth.*" *North and Thomas (1973)*
* Factors accumulation are *proximate causes* of economic growth.

*"The factors we have listed (innovation, economies of scale, education, capital accumulation, etc.) are not causes of growth; they are growth."* North and Thomas (1973)

* Correlation ≠ causality.

* Why different choices of investment?

* What are the *fundamental* causes of economic growth?
Fundamental causes of long-run growth
Four common hypotheses:

* Luck: multiple equilibria and path dependence.

* Geography and climate

* Cultural norms of behavior.

* Domestic institutions.
* Roots: coordination failures, credit market imperfections, etc.
* Roots: coordination failures, credit market imperfections, etc.

* Prisoner's dilemma type game (e.g. technological complementarities)

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Everybody else</th></tr><tr><th>High investment</th><th>Low investment</th></tr></thead><tbody><tr><td rowspan="2">Individual</td><td>High investment</td><td>y<sup>H</sup>, y<sup>H</sup></td><td>y<sup>L</sup> - ε, y<sup>L</sup></td></tr><tr><td>Low investment</td><td>y<sup>L</sup>, y<sup>L</sup> - ε'</td><td>y<sup>L</sup>, y<sup>L</sup></td></tr></tbody></table>

* $y^H > y^L$ and $\varepsilon, \varepsilon' > 0$.
* Roots: coordination failures, credit market imperfections, etc.

* Prisoner's dilemma type game (e.g. technological complementarities)

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Everybody else</th></tr><tr><th>High investment</th><th>Low investment</th></tr></thead><tbody><tr><td rowspan="2">Individual</td><td>High investment</td><td>y<sup>H</sup>, y<sup>H</sup></td><td>y<sup>L</sup> - &epsilon;, y<sup>L</sup></td></tr><tr><td>Low investment</td><td>y<sup>L</sup>, y<sup>L</sup> - &epsilon;'</td><td>y<sup>L</sup>, y<sup>L</sup></td></tr></tbody></table>

* Two symmetric (pure-strategy) equilibria.
* Path dependence: remain stuck in inferior equilibrium.
* Path dependence: remain stuck in inferior equilibrium.

* Luck cannot explain:

* Hundred years of cumulative processes.

* Hundred years of divergence.

* Why some countries can have very rapid growth.
* Path dependence: remain stuck in inferior equilibrium.

* Luck cannot explain:

* Hundred years of cumulative processes.

* Hundred years of divergence.

* Why some countries can have very rapid growth.

* Institutions as coordination device for equilibrium selection.

*Myerson (2009)*
* Geography hypothesis 1: Montesquieu (1748).

* Climate (heat) shape efforts and attitudes:

> "The heat of the climate can be so excessive that the body there will be absolutely without strength. So, prostration will pass even to the spirit; no curiosity, no noble enterprise, no generous sentiment; inclinations will all be passive there; laziness there will be happiness. People are [...] more vigorous in cold climates. The inhabitants of warm countries are like old men, timorous; the people in cold countries are like young men, brave."
* Very borderline...

* Some serious economists were still convinced in late 19th century:

> "[V]igor depends partly on race qualities: but these, so far as they can be explained at all, seem to be chiefly due to climate."
> 
> *Marshall (1890)*

* Non-credible hypothesis.
* Geography hypothesis 2: impact of geography on agriculture and technology.
* Geography hypothesis 2: impact of geography on agriculture and technology.

"[S]erious study of the problems of underdevelopment [...] should take into account the climate and its impacts on soil, vegetation, animals, humans and physical assets—in short, on living conditions in economic development." *Myrdal (1968)*

* Geographical differences ⇒ timing and nature of neolithic revolution ⇒ social organization and development. *Diamond (1997)*
Geographical determinism not plausible.
Geographical determinism not plausible.

* Comparative advantage.

* Low agricultural productivity ⇒ comparative advantage in industry.

* Unfavorable geography provides incentives to industrialize earlier.
Geographical determinism not plausible.

* Comparative advantage.

* Low agricultural productivity ⇒ comparative advantage in industry.

* Unfavorable geography provides incentives to industrialize earlier.

* Necessary threshold in agricultural productivity?

* Many failed to industrialize, although ahead in agricultural productivity.
Geographical determinism not plausible.

* Comparative advantage.

* Low agricultural productivity ⇒ comparative advantage in industry.

* Unfavorable geography provides incentives to industrialize earlier.

* Necessary threshold in agricultural productivity?

* Many failed to industrialize, although ahead in agricultural productivity.

* But potentially large complementarities with institutions.
* Values, preferences, beliefs matter for economic performance.
* Values, preferences, beliefs matter for economic performance.

* Famous example: Protestant origins of capitalism. Weber 1930

"Montesquieu says of the English that they 'had progressed the farthest of all peoples in the world in three important things: in piety, in commerce, and in freedom.' Is it not possible that their commercial superiority and their adaptation to the free political institutions are connected in some ways with that record of piety which Montesquieu ascribes to them?"
* Protestant origins of capitalism:

* Protestantism: hard work, thrift, saving.

* Economic success interpreted as chosen by God.
* Protestant origins of capitalism:

* Protestantism: hard work, thrift, saving.

* Economic success interpreted as chosen by God.

* Alternative argument: culture of (non-)cooperation.
* Protestant origins of capitalism:

* Protestantism: hard work, thrift, saving.

* Economic success interpreted as chosen by God.

* Alternative argument: culture of (non-)cooperation.

* Culture as a coordination mechanism for equilibrium selection.
* Protestant origins of capitalism:

* Protestantism: hard work, thrift, saving.

* Economic success interpreted as chosen by God.

* Alternative argument: culture of (non-)cooperation.

* Culture as a coordination mechanism for equilibrium selection.

* Difficult to explain growth miracle. How fast is cultural change?
* Protestant origins of capitalism:

* Protestantism: hard work, thrift, saving.

* Economic success interpreted as chosen by God.

* Alternative argument: culture of (non-)cooperation.

* Culture as a coordination mechanism for equilibrium selection.

* Difficult to explain growth miracle. How fast is cultural change?

* Complementarities between culture and institutions.
"Institutions are the rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction. In consequence [institutions] structure incentives in human exchange, whether political, social, or economic." *North (1990)*
"Institutions are the rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction. In consequence [institutions] structure incentives in human exchange, whether political, social, or economic." *North (1990)*

* Key elements:

* Humanly devised.

* Place constraints on individual behavior.

* Shape human interaction and affect incentives.

* Institutions influence economic, political and social relations among households, individuals and firms.
* Examples:

* Property rights.

* Functioning markets.

* Contractual opportunities.
* Examples:

* Property rights.

* Functioning markets.

* Contractual opportunities.

* What economic institutions do:

* Shape the structure of economic incentives.

* Ensure allocation of resources to efficient uses.

* Determine distribution of economic revenues.
* Examples:

* Property rights.

* Functioning markets.

* Contractual opportunities.

* What economic institutions do:

* Shape the structure of economic incentives.

* Ensure allocation of resources to efficient uses.

* Determine distribution of economic revenues.

* “Good” economic institutions stimulate proximate causes of growth:

* Physical capital accumulation.

* Human capital accumulation.

* Development of better technologies.

* Usually: enforcement of property rights and equality of opportunity.
* Why societies not adopt all “good” economic institutions?

* Coordination failures and multiple equilibria.

* Conflicts of interest within society (Schumpeterian creative destruction).

* Distribution and conflict shape institutions.
Topic 2

The impact of institutions on long-run development
Do institutions matter for long-run development?
* Correlations between institutions and development.

* Reversal of fortune. Acemoglu, Johnson and Robinson (2002)

* Colonial origins of comparative development.
  Acemoglu, Johnson and Robinson (2001)

* Unbundling institutions. Acemoglu and Johnson (2005)
* We have seen measures of development.

* Focus on log GDP per capita (PWT, Maddison).
* We have seen measures of development.

* Focus on log GDP per capita (PWT, Maddison).

* (Previously) Main sources for measures of institutions:

* Polity IV.

* Freedom House.

* World Bank Governance Indicators.
* We have seen measures of development.

* Focus on log GDP per capita (PWT, Maddison).

* (Previously) Main sources for measures of institutions:

* Polity IV.

* Freedom House.

* World Bank Governance Indicators.

* Best current source: V-Dem.

* Some measures of quality of formal institutions (see codebook):

* Access to justice.

* Property rights.

* Rule of law.
* Best cross-country institutions data source: V-Dem.

* 450+ indicators, 81 indices, 202 countries, 1789–2018.

* Consistent coding, extremely complete.

* Vastly superior to alternatives.
Data Source: Varieties of Democracy

* Property rights (v2xcl_prpty).

* Question: Do citizens enjoy the right to private property?

* Scale: Ordinal, converted to interval by the measurement model, from low to high 0–1.
Data Source: Varieties of Democracy

* Property rights (v2xcl_prpty).

* Question: Do citizens enjoy the right to private property?

* Scale: Ordinal, converted to interval by the measurement model, from low to high 0–1.

* Access to justice (v2xcl_acjst).

* Question: Do citizens enjoy secure and effective access to justice?

* Scale: Ordinal, converted to interval by the measurement model, from low to high 0–1.
* Property rights (v2xcl_prpty).

* Question: Do citizens enjoy the right to private property?

* Scale: Ordinal, converted to interval by the measurement model, from low to high 0–1.

* Access to justice (v2xcl_acjst).

* Question: Do citizens enjoy secure and effective access to justice?

* Scale: Ordinal, converted to interval by the measurement model, from low to high 0–1.

* Rule of Law (v2x_rule).

* Question: To what extent are laws transparently, independently, predictably, impartially, and equally enforced, and to what extent do the actions of government officials comply with the law?

* Scale: Interval, from low to high (0–1). Index combining 15 indicators.
• Polity IV Project (1800–2015).

• Freedom House (1973–2018).

• Political Regimes (1800–2015). Boix et al. (2012)

• Word bank governance indicators (1996–2017). Kaufmann et al. (2010)

• Lexical Index of Electoral Democracy (1800–2013). Skaaning et al. (2015)

• Unified Democracy Score (1946–2012). Penstein et al. (2010)

• Political Institutions and Events (1917–2013). Przeworski et al. (2013)
<figure><img src="image_32.png" /><figcaption>Log GDP per Capita and Property Rights, 2016</figcaption></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
<figure><img src="image_33.png" /><figcaption>Log GDP per Capita and Access to Justice, 2016</figcaption></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
<figure><img src="image_34.png" /><figcaption>Log GDP per Capita and Rule of Law, 2016</figcaption></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
Correlations between Institutions and Development

$$Y_{cy} = \alpha + \beta X_{cy} + \varepsilon_{cy}$$

<table><thead><tr><th>Year</th><th>Property</th><th>Justice</th><th>Law</th><th>Countries</th></tr></thead><tbody><tr><td>1960</td><td>1.64***</td><td>1.73***</td><td>1.78***</td><td>133</td></tr><tr><td>1980</td><td>1.84***</td><td>1.81***</td><td>1.83***</td><td>140</td></tr><tr><td>2000</td><td>2.78***</td><td>2.77***</td><td>2.66***</td><td>162</td></tr><tr><td>2016</td><td>2.60***</td><td>2.27***</td><td>2.16***</td><td>160</td></tr></tbody></table>

Source: V-Dem 9, Maddison Project Database 2018.

Dependent variable: *log* GDP per capita.

*** Significant at the 1 percent level.
* Can we interpret these relationships as causal?
* Can we interpret these relationships as causal?

* Identification issues:

* Reverse causality: wealthy countries could afford better institutions.

* Omitted variable bias: Z causes both institutions and development.
* Can we interpret these relationships as causal?

* Identification issues:

* Reverse causality: wealthy countries could afford better institutions.

* Omitted variable bias: Z causes both institutions and development.

* General solution: find exogenous source of variation.
<figure><img src="image_35.png" /><figcaption>Log GDP per Capita and Latitude, 2016</figcaption></figure>

Source: Maddison Project Database 2018, CEPII GeoDist, 172 countries.
<figure><img src="image_36.png" /><figcaption>Rule of Law and Latitude, 2016</figcaption></figure>

Source: V-Dem 9, CEPII GeoDist, 172 countries.
* Best cross-country geographical data source: [CEPII-GeoDist](#).

* Geographical variables: continent, landlocked, official language, area, latitude, longitude, identity of colonizers.

* Diadic dataset: bilateral distance.

* Reference paper: Mayer and Zigago (2011)
<figure><img src="image_37.png" /><figcaption>Rule of Law in the Koreas</figcaption></figure>

Source: V-Dem 9.
<figure><img src="image_38.png" /><figcaption>## GDP per Capita in the Koreas</figcaption></figure>

Source: Maddison Project Database 2018.
# Reversal of Fortune

* Acemoglu, Johnson and Robinson (2002)

* Use colonization as a quasi-natural experiment to assess the causal impact of institutions on long-run economic development.
* Acemoglu, Johnson and Robinson (2002)

* Use colonization as a quasi-natural experiment to assess the causal impact of institutions on long-run economic development.

* Question: richest ancient civilizations are today among poorest nations. Why?
* Acemoglu, Johnson and Robinson (2002)

* Use colonization as a quasi-natural experiment to assess the causal impact of institutions on long-run economic development.

* Question: richest ancient civilizations are today among poorest nations. Why?

* Document reversal of relative income among former colonies.

* Evidence that institutions matter for long-run economic development.

* Geography, culture, luck cannot account for the reversal.
* Which countries in this experiment?

* Colonized by Europeans between 15th and 19th centuries.

* Excludes Ireland, Russia, Middle East.

* How to capture prosperity before colonization (1500)?

* Maddison data: too little sample size.

* Urbanization: captures high agricultural productivity. Bairoch (1998)

* Population density.

* Sample sizes small when paper written.
<figure><img src="image_39.png" /><figcaption>## Urbanization and Log GDP per Capita in 2017</figcaption></figure>

Source: Penn World Table 9, WB, 175 countries.
<figure><img src="image_40.png" /><figcaption>Population Density and Log GDP per Capita in 2017</figcaption></figure>

Source: Penn World Table 9, CEPII GeoDist, 175 countries.
## Urbanization in 1500 and Log GDP per Capita in 1995
Former European Colonies

<figure><img src="image_41.png" /></figure>

Source: Penn World Table 9, CEPII GeoDist. AJR (2002), 40 countries.
<figure><img src="image_42.png" /><figcaption>FIGURE I
Log GDP per Capita (PPP) in 1995 against Urbanization Rate in 1500</figcaption></figure>

*Note.* GDP per capita is from the World Bank [1999]; urbanization in 1500 is people living in towns with more than 5000 inhabitants divided by total population, from Bairoch [1988] and Eggimann [1999]. Details are in Appendices 1 and 2.
<figure><img src="image_43.png" /><figcaption>## Urbanization in 1500 and Log GDP per Capita in 2017
Former European Colonies</figcaption></figure>

Source: Penn World Table 9, CEPII GeoDist. AJR (2002), 40 countries.
Reversal of Fortune: Main Result

<figure><img src="image_44.png" /><figcaption>FIGURE II
Log GDP per Capita (PPP) against Log Population Density in 1500</figcaption></figure>

*Note.* GDP per capita from the World Bank [1999]; log population density in 1500 from McEvedy and Jones [1978]. Details are in Appendix 2.
# Reversal of Fortune: Robustness

* Potential confounding factors:

* Geography: continent, latitude, landlocked, island, temperature.

* Natural resources: gold, iron, silver, zinc, oil, coal.

* Colonizer: French, Spanish, British.

* Religion: Catholicism, Islam.
# Reversal of Fortune: Robustness

## Potential confounding factors:

* Geography: continent, latitude, landlocked, island, temperature.

* Natural resources: gold, iron, silver, zinc, oil, coal.

* Colonizer: French, Spanish, British.

* Religion: Catholicism, Islam.

## Robust relationship:

* Higher urbanization in 1500 ⇒ lower income in 1995.

* In former European colonies.
# Reversal of Fortune: Robustness

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">log GDP per capita (PPP) in 1995</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Urbanization 1500</td><td>-8.56***<br>[2.35]</td><td>-11.79**<br>[4.74]</td><td>-8.41***<br>[3.04]</td><td>-10.59**<br>[4.71]</td></tr><tr><td>Geography</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Natural resources</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Colonizer</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Religion</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>R2</td><td>0.22</td><td>0.70</td><td>0.31</td><td>0.79</td></tr><tr><td>Countries</td><td>40</td><td>40</td><td>40</td><td>40</td></tr></tbody></table>

*** Significant at the 1 percent level. ** Significant at the 5 percent level.
* Interpreting a coefficient of -10.

* 10 pp lower urbanization 1500 ⇒ 1 log higher income 1995.

* Taking exp, means 2-3 times richer.
* Interpreting a coefficient of -10.

* 10 pp lower urbanization 1500 ⇒ 1 log higher income 1995.

* Taking exp, means 2-3 times richer.

* Not reversion to mean: specific to former colonies.
<figure><img src="image_45.png" /><figcaption>## Urbanization in 1500 and Log GDP per Capita in 1995
### Non-Colonies</figcaption></figure>

Source: AJR (2002), 43 countries.
## Urbanization in 1500 and 1000
Former European Colonies

<figure><img src="image_46.png" /></figure>

Source: AJR (2002), 30 countries.
# Reversal of Fortune: Timing

<figure><img src="image_47.png" /></figure>
* Facts compatible with which theory?
* Facts compatible with which theory?

* Geography hypotheses.

* Culture.

* Institutions.
* Simple geography hypothesis.

* Permanent effects of geography ($\alpha_1$) $\Rightarrow$ prosperity.

$$Y_{it} = \alpha_0 + \alpha_1 \cdot G_i + \nu_t + \varepsilon_{it}$$
* Simple geography hypothesis.

* Permanent effects of geography ($α_1$) ⇒ prosperity.

$$Y_{it} = α_0 + α_1 \cdot G_i + ν_t + ε_{it}$$

* Inconsistent: rich countries in 1500 are poor today.
* Sophisticated geography hypothesis.

* Time-varying effects of geography ($\alpha_{2t}$) $\Rightarrow$ prosperity.

$$Y_{it} = \alpha_0 + \alpha_1 \cdot G_i + \sum_t \alpha_{2t} \cdot \nu_t \cdot G_i + \nu_t + \varepsilon_{it}$$
* Sophisticated geography hypothesis.

* Time-varying effects of geography ($\alpha_{2t}$) $\Rightarrow$ prosperity.

$$Y_{it} = \alpha_0 + \alpha_1 \cdot G_i + \sum_t \alpha_{2t} \cdot \nu_t \cdot G_i + \nu_t + \varepsilon_{it}$$

* "Latitude-specific" agricultural technologies: reversal is industry based.
* Sophisticated geography hypothesis.

* Time-varying effects of geography ($\alpha_{2t}$) $\Rightarrow$ prosperity.

$$Y_{it} = \alpha_0 + \alpha_1 \cdot G_i + \sum_t \alpha_{2t} \cdot \nu_t \cdot G_i + \nu_t + \varepsilon_{it}$$

* "Latitude-specific" agricultural technologies: reversal is industry based.

* Lack of resources endowment for industrialization: not correlated.
* Culture hypothesis.

* Colonialism might have changed culture, created new ones.

* But has nothing to say about timing.
<figure><img src="image_48.png" /><figcaption>## Urbanization in 1500 and Property Rights in 2016
Former European Colonies</figcaption></figure>

Source: V-Dem 9, AJR (2002), 40 countries.
## Urbanization in 1500 and Access to Justice in 2016
Former European Colonies

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

Source: V-Dem 9, AJR (2002), 40 countries.
## Urbanization in 1500 and Rule of Law in 2016
Former European Colonies

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

Source: V-Dem 9, AJR (2002), 40 countries.
# Reversal of Fortune: Institutions

* Evidence of institutional reversal.

* High urbanization in 1500 ⇒ “extractive institutions.”

* Low urbanization in 1500 ⇒ “inclusive institutions.”
# Reversal of Fortune: Institutions

* Evidence of institutional reversal.

* High urbanization in 1500 ⇒ "extractive institutions."

* Low urbanization in 1500 ⇒ "inclusive institutions."

* Explains also the timing:

* "Good institutions" ⇒ incentives for K and HK accumulation.

* Institutions are more important when opportunity to industrialize.
* Factors for choice of colonial institutions:
* Factors for choice of colonial institutions:

* Economic profitability of extractive institutions.

* Dense population ⇒ large supply of labor.

* Pre-existing system of tax administration ⇒ continue.
* Factors for choice of colonial institutions:

* Economic profitability of extractive institutions.

* Dense population ⇒ large supply of labor.

* Pre-existing system of tax administration ⇒ continue.

* Whether Europeans could settle.

* Low density ⇒ Europeans settle ⇒ protect property through inclusive institutions.

* Same institutions as home ⇒ incentives for Europeans to settle.
* Acemoglu, Johnson and Robinson (2001)

* Show that disease environment shaped incentives to settle.

* Use settler mortality as an IV for current institutions.

* Show causal impact of current institutions on current development.
Causal chain in “Colonial Origins”

(potential) settler → settlements
mortality

⇒ early → current
institutions → institutions↓

⇒ current
performance.
Log GDP per Capita and Rule of Law, 2016. Causal?

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

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
<figure><img src="image_52.png" /><figcaption>FIGURE 2. OLS RELATIONSHIP BETWEEN EXPROPRIATION RISK AND INCOME</figcaption></figure>
* Impact of current institutions on current economic performance:

$$ \log y_i = \mu + \alpha R_i + \mathbf{X}'_i \gamma + \varepsilon_i $$
* Impact of current institutions on current economic performance:

$$ \log y_i = \mu + \alpha R_i + \mathbf{X}_i' \gamma + \varepsilon_i $$

* First stage: impact of settler mortality on current institutions.

$$ R_i = \zeta + \beta \log M_i + \mathbf{X}_i' \delta + \nu_i $$

* y: income per capita.

* R: measure of institutions.

* X: controls.

* M: settler mortality rate.
* Impact of current institutions on current economic performance:

$$ \log y_i = \mu + \alpha R_i + \mathbf{X}'_i \gamma + \varepsilon_i $$

* First stage: impact of settler mortality on current institutions.

$$ R_i = \zeta + \beta \log M_i + \mathbf{X}'_i \delta + \nu_i $$

* y: income per capita.

* R: measure of institutions.

* X: controls.

* M: settler mortality rate.

* Exclusion restriction: settler mortality only affect development through institutions.
Settler Mortality and Rule of Law, 2016
Former European Colonies

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

Source: V-Dem 9, AJR (2001), 62 countries.
<figure><img src="image_54.png" /><figcaption>FIGURE 3. FIRST-STAGE RELATIONSHIP BETWEEN SETTLER MORTALITY AND EXPROPRIATION RISK</figcaption></figure>
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Rule of Law in 2016</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Log settler mortality</td><td>-0.11***<br>[0.02]</td><td>-0.05*<br>[0.03]</td><td>-0.10***<br>[0.02]</td><td>-0.05*<br>[0.03]</td></tr><tr><td>Geography</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Colonizer</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>R2</td><td>0.23</td><td>0.29</td><td>0.28</td><td>0.35</td></tr><tr><td>Countries</td><td>62</td><td>62</td><td>62</td><td>62</td></tr></tbody></table>

*** Significant at the 1 percent level. * Significant at the 1 percent level.
<figure><img src="image_55.png" /><figcaption>Settler Mortality and Log GDP per Capita, 2016
Former European Colonies</figcaption></figure>

Source: Maddison Project Database 2018, AJR (2001), 62 countries.
<figure><img src="image_56.png" /><figcaption>FIGURE 1. REDUCED-FORM RELATIONSHIP BETWEEN INCOME AND SETTLER MORTALITY</figcaption></figure>
Colonial Origins: Reduced Form

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Log GDP per Capita in 2016</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Log settler mortality</td><td>-0.63***<br>[0.08]</td><td>-0.42*<br>[0.13]</td><td>-0.58***<br>[0.09]</td><td>-0.44***<br>[0.13]</td></tr><tr><td>Geography</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Colonizer</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>R2</td><td>0.52</td><td>0.64</td><td>0.57</td><td>0.67</td></tr><tr><td>Countries</td><td>61</td><td>61</td><td>61</td><td>61</td></tr></tbody></table>

*** Significant at the 1 percent level. * Significant at the 1 percent level.
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Log GDP per Capita in 2016</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Rule of Law in 2016</td><td>5.80***<br>[0.84]</td><td>6.88*<br>[2.28]</td><td>5.84***<br>[0.92]</td><td>7.17***<br>[2.57]</td></tr><tr><td>Geography</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Colonizer</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries</td><td>61</td><td>61</td><td>61</td><td>61</td></tr></tbody></table>

*** Significant at the 1 percent level. * Significant at the 1 percent level.
* Once institutions purged, little role of geography.

* Rationale for raw correlation between development and geography:

* Europeans had little immunity to tropical diseases.

* They tended to settle in temperate latitudes.

* Explains how geography partly shaped historical institutions.

* Geography matters only through institutions.
* Once institutions purged, little role of geography.

* Rationale for raw correlation between development and geography:

* Europeans had little immunity to tropical diseases.

* They tended to settle in temperate latitudes.

* Explains how geography partly shaped historical institutions.

* Geography matters only through institutions.

* Colonial culture? No effect of identity of colonizer.
* Large literature resulting from these two papers.

* Criticisms, e.g. Albouy (2012) (after 5 unpublished comments).

* Reliability and comparability of European settler mortality rates.

* Procedure of clustering standard errors.

* Weak instrument problem.

* Responses in AJR (2012) (after 3 unpublished replies).

* Big picture is robust.
* North (1981) distinguishes:

* Contract theory of the state: legal framework =⇒ private contracts.

* Predatory theory of the state: instrument for transferring resources.
* North (1981) distinguishes:

* Contract theory of the state: legal framework =⇒ private contracts.

* Predatory theory of the state: instrument for transferring resources.

* Acemoglu and Johnson's (2005) formulation:

* Contracting institutions: support private contracts.

* Property rights institutions: constrain government expropriation.
* North (1981) distinguishes:

* Contract theory of the state: legal framework =⇒ private contracts.

* Predatory theory of the state: instrument for transferring resources.

* Acemoglu and Johnson's (2005) formulation:

* Contracting institutions: support private contracts.

* Property rights institutions: constrain government expropriation.

* Empirical importance of each institutional feature?
* Contracting institutions (horizontal).

* Rules governing contracting between ordinary citizens.

* Main component: functioning of legal system.

* Affects cost of enforcing contracts.

* Implications for equilibrium contracts and transactions.
* Contracting institutions (horizontal).

* Rules governing contracting between ordinary citizens.

* Main component: functioning of legal system.

* Affects cost of enforcing contracts.

* Implications for equilibrium contracts and transactions.

* Property rights institutions (vertical).

* Rules protecting citizens against government and elites power.

* Relate to interactions between state and society.

* Affects returns to investment.

* Implications for equilibrium capital accumulation.
* Log GDP per capita. PWT

* Investment to GDP. PWT

* Private credit to GDP. WB

* Stock market capitalization to GDP. Beck et al. (2001)
Measurement: Contracting Institutions

• Index of legal formalism (1–7). Djankov et al. (2003)

• Overall procedural complexity of resolving court case (0–10). WB

• Number of procedures for resolving court case. WB
# Measurement: Property Rights Institutions

* Constraint on executive (1–7). Polity IV

* Protection against expropriation (1–10). Political Risk Services

* Private property index (1–5). Heritage Foundation

* Property rights (0–1). V-Dem
<figure><img src="image_57.png" /><figcaption>Log GDP per Capita and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 108 countries.
<figure><img src="image_58.png" /><figcaption>Log GDP per Capita and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 121 countries.
<figure><img src="image_59.png" /><figcaption>Log GDP per Capita and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 122 countries.
<figure><img src="image_60.png" /><figcaption>Log GDP per Capita and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 145 countries.
<figure><img src="image_61.png" /><figcaption>Log GDP per Capita and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 110 countries.
<figure><img src="image_62.png" /><figcaption>Log GDP per Capita and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 129 countries.
<figure><img src="image_63.png" /><figcaption>Investment to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 110 countries.
<figure><img src="image_64.png" /><figcaption>Investment to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 124 countries.
<figure><img src="image_65.png" /><figcaption>Investment to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 125 countries.
<figure><img src="image_66.png" /><figcaption>Investment to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 149 countries.
## Investment to GDP and Property Rights, 1990s
All Countries

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

Source: AJ (2005), 113 countries.
<figure><img src="image_68.png" /><figcaption>Investment to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 131 countries.
<figure><img src="image_69.png" /><figcaption>Credit to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 103 countries.
<figure><img src="image_70.png" /><figcaption>Credit to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 117 countries.
<figure><img src="image_71.png" /><figcaption>Credit to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 118 countries.
<figure><img src="image_72.png" /><figcaption>Credit to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 140 countries.
<figure><img src="image_73.png" /><figcaption>Credit to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 109 countries.
<figure><img src="image_74.png" /><figcaption>Credit to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 126 countries.
<figure><img src="image_75.png" /><figcaption>## Capitalization to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 89 countries.
<figure><img src="image_76.png" /><figcaption>Capitalization to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 91 countries.
<figure><img src="image_77.png" /><figcaption>## Capitalization to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 91 countries.
# Correlations: Capitalization and Property Rights

<figure><img src="image_78.png" /><figcaption>Capitalization to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 105 countries.
<figure><img src="image_79.png" /><figcaption>Capitalization to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 95 countries.
<figure><img src="image_80.png" /><figcaption>Capitalization to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 102 countries.
* Outcome: focus on Log GDP per capita.

* Contracting institutions: focus on legal formalism.

* Property rights institutions: focus on constraint on executive.

* Variables measured in the 1990s.
$$Y_c = \alpha F_c + \beta I_c + \mathbf{Z}'_c \gamma + \varepsilon_c$$

* $Y_c$: outcome of interest in country c.

* $F_c$: measure of contracting institutions.

* $I_c$: measure of property rights institutions.

* $\mathbf{Z}_c$: vector of controls (geography, religion...).
Univariate OLS Coefficients

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Log GDP per capita</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Legal formalism</td><td>-0.28***<br>[0.08]</td><td>-0.21**<br>[0.11]</td><td></td><td></td></tr><tr><td>Constraint on executive</td><td></td><td></td><td>0.33***<br>[0.04]</td><td>0.30***<br>[0.05]</td></tr><tr><td>Sample</td><td>All</td><td>FEC</td><td>All</td><td>FEC</td></tr><tr><td>Countries</td><td>108</td><td>65</td><td>145</td><td>86</td></tr></tbody></table>

Source: AJ (2005). FEC: Former European Colonies.

*** Significant at the 1 percent level. ** Significant at the 5 percent level.
* Threats to identification.
* Threats to identification.

* Reverse causality.

* Omitted variable bias (e.g. geography).
* Threats to identification.

* Reverse causality.

* Omitted variable bias (e.g. geography).

* Measurement error.

* Downward attenuation bias.

* Correlated measurement error.
* Solution: two-stage least squares (2SLS).

* Two instruments:
* Solution: two-stage least squares (2SLS).

* Two instruments:

* Contracting IV: British legal origins. La Porta et al. (1997, 1998)
* Solution: two-stage least squares (2SLS).

* Two instruments:

* Contracting IV: British legal origins. La Porta et al. (1997, 1998)

* Property rights IV: European settlers mortality. AJR (2001)
$$F_c = \delta_1 L_c + \eta_1 M_c + \mathbf{Z}'_c \gamma_1 + u_{1c}$$

$$I_c = \delta_2 L_c + \eta_2 M_c + \mathbf{Z}'_c \gamma_2 + u_{2c}$$

* $F_c$: measure of contracting institutions.

* $I_c$: measure of property rights institutions.

* $L_c$: British colony indicator.

* $M_c$: log European settlers mortality.

* $\mathbf{Z}_c$: vector of controls (geography, religion...).
$$F_c = \delta_1 L_c + \eta_1 M_c + \mathbf{Z}'_c \gamma_1 + u_{1c}$$

$$I_c = \delta_2 L_c + \eta_2 M_c + \mathbf{Z}'_c \gamma_2 + u_{2c}$$

* $F_c$: measure of contracting institutions.

* $I_c$: measure of property rights institutions.

* $L_c$: British colony indicator.

* $M_c$: log European settlers mortality.

* $\mathbf{Z}_c$: vector of controls (geography, religion...).

* Exclusion restriction: $Cov(\varepsilon_c, L_c) = Cov(\varepsilon_c, I_c) = 0$.

* $\varepsilon_c$: error term from second-stage.
* Log settler mortality in former European colonies. AJR (2001)

* European colonization strategy ⇌ historical property rights institutions ⇌ current property rights institutions.

* Disease environment ⇌ settlers mortality ⇌ colonization strategy.
* British legal origin. La Porta et al. (1997, 1998)

* Two great legal traditions:

* Common-law countries (British): low formalism.

* Civil-law countries (French, German, Scandinavian): high formalism.
* British legal origin. La Porta et al. (1997, 1998)

* Two great legal traditions:

* Common-law countries (British): low formalism.

* Civil-law countries (French, German, Scandinavian): high formalism.

* Exogenous to countries colonized by Europeans.
* British legal origin. La Porta et al. (1997, 1998)

* Two great legal traditions:

* Common-law countries (British): low formalism.

* Civil-law countries (French, German, Scandinavian): high formalism.

* Exogenous to countries colonized by Europeans.

* Exclusion restriction:

* Might affect $Y_c$ through channels other than contracting.

* Expect $\text{Cov}(\varepsilon_c, L_c) \ge 0 \Rightarrow$ upper bound.
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Legal Formalism</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>English legal origin</td><td>-2.13***<br>[0.23]</td><td>-2.04***<br>[0.23]</td><td></td></tr><tr><td>Log settler mortality</td><td></td><td>0.36**<br>[0.14]</td><td>0.12<br>[0.08]</td></tr><tr><td>R2</td><td>0.64</td><td>0.11</td><td>0.65</td></tr><tr><td>Countries</td><td>54</td><td>54</td><td>54</td></tr></tbody></table>

Source: AJ (2005).

*** Significant at the 1 percent level. ** Significant at the 5 percent level.
Partial Correlations

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

Source: AJ (2005), 54 countries.

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

Source: AJ (2005), 54 countries.
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Constraint on Executive</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>English legal origin</td><td>0.65<br>[0.49]</td><td></td><td>0.15<br>[0.42]</td></tr><tr><td>Log settler mortality</td><td></td><td>-0.77***<br>[0.15]</td><td>-0.75***<br>[0.16]</td></tr><tr><td>R2</td><td>0.03</td><td>0.25</td><td>0.26</td></tr><tr><td>Countries</td><td>72</td><td>72</td><td>72</td></tr></tbody></table>

Source: AJ (2005).

*** Significant at the 1 percent level. ** Significant at the 5 percent level.
Partial Correlations

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

Source: AJ (2005), 72 countries.

<figure><img src="image_84.png" /></figure>

Source: AJ (2005), 72 countries.
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Log GDP per capita</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>Legal formalism</td><td>-0.16*<br>[0.10]</td><td>0.05<br>[0.23]</td></tr><tr><td>Constraint on executive</td><td>0.31***<br>[0.07]</td><td>0.99***<br>[0.27]</td></tr><tr><td>Estimator</td><td>OLS</td><td>2SLS</td></tr><tr><td>Countries</td><td>51</td><td>51</td></tr></tbody></table>

Source: AJ (2005).

*** Significant at the 1 percent level. * Significant at the 1 percent level.
* Similar results with other measures of institutions.
* Similar results with other measures of institutions.

* Investment: property rights matter but not contracting.
* Similar results with other measures of institutions.

* Investment: property rights matter but not contracting.

* Credit: property rights matter but not contracting.
* Similar results with other measures of institutions.

* Investment: property rights matter but not contracting.

* Credit: property rights matter but not contracting.

* Stock market: contracting matters but not property rights.
* Robust to semi-reduced form: legal origin enters second stage directly.
* Robust to semi-reduced form: legal origin enters second stage directly.

* Robust to alternative samples.
* Robust to semi-reduced form: legal origin enters second stage directly.

* Robust to alternative samples.

* Robust to controls (geography, religion, macro variables...).
* Robust to semi-reduced form: legal origin enters second stage directly.

* Robust to alternative samples.

* Robust to controls (geography, religion, macro variables...).

* Robust to alternative measurement of institutions: firms surveys.
* Contracting institutions have limited consequences (flexibility).

* Property rights institutions matter much more (necessary condition).
Long-term economic impact of colonial rule?
* Many aspects to this question:

* Excessive exploitation of resources (-).

* Growth dependence (-).

* Labor coercion (-).

* Schooling systems (+).

* Capital movements (+).

* Not even major force: geography hypotheses.
* Many aspects to this question:

* Excessive exploitation of resources (-).

* Growth dependence (-).

* Labor coercion (-).

* Schooling systems (+).

* Capital movements (+).

* Not even major force: geography hypotheses.

* Focus on important aspect:

* Persistent role of (extractive) colonial institutions.
Differences in Outcomes across Colonial Status

<figure><img src="image_85.png" /><figcaption>Log GDP per Capita and Rule of Law across Colonial Status in 2016</figcaption></figure>

Source: V-Dem 9, GeoDist, 160 countries.
<figure><img src="image_86.png" /><figcaption>Log GDP per Capita and Rule of Law across Colonial Status in 2016</figcaption></figure>
# Differences in Outcomes across Colonial Status

<figure><img src="image_87.png" /><figcaption>Log GDP per Capita and Rule of Law across Colonial Status in 2016</figcaption></figure>

Source: V-Dem 9, GeoDist, 160 countries.
## Reversal of Fortune among Former Colonies (AJR 2002)

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

Source: Penn World Table 9, CEPII GeoDist. AJR (2002), 40 countries.
## No Reversal of Fortune among Non-Colonies (AJR 2002)

<figure><img src="image_89.png" /></figure>

Source: AJR (2002), 43 countries.
* AJR 2001 ⇒ extractive colonial institutions (in high mortality areas)
  ⇒ persistence of weak institutions
* AJR 2001 ⇒ extractive colonial institutions (in high mortality areas) ⇒ persistence of weak institutions

* But AJR 2001 does not settle the debate.

① Unwarranted cross-country comparisons?
* AJR 2001 ⇒ extractive colonial institutions (in high mortality areas) ⇒ persistence of weak institutions

* But AJR 2001 does not settle the debate.

① Unwarranted cross-country comparisons?

② Black box of institutions: what are the mechanisms?
* AJR 2001 ⇒ extractive colonial institutions (in high mortality areas) ⇒ persistence of weak institutions

* But AJR 2001 does not settle the debate.

① Unwarranted cross-country comparisons?

② Black box of institutions: what are the mechanisms?

* Iyer (2010), Dell (2010), and Dell and Olken (2020)

① Use within-country variation in colonial institutions.

② Precise exploration of mechanisms.

⇒ Better and more subtle answer to the question.
The Review of Economics and Statistics

VOL. XCII

NOVEMBER 2010

NUMBER 4

DIRECT VERSUS INDIRECT COLONIAL RULE IN INDIA:
LONG-TERM CONSEQUENCES

Lakshmi Iyer*
* Iyer (2010)

* What are the long-term economic consequences of British colonial rule in India?
* Iyer (2010)

* What are the long-term economic consequences of British colonial rule in India?

* Do Indian districts that were under direct colonial rule have better agricultural productivity and public goods provision today than those that were under indirect colonial rule?
Three periods of British conquest of India.

① The Ring Fence (1765–1818).
East India Company annexed 192 districts through wars.

② Subordinate Isolation (1818–1858).
Native states subordinate to British. 79 districts annexed.

③ End of Annexation (1857–1947).
British Crown takes over. 1 district annexed.

⇒ In 1947, 260 districts (65%) under direct British rule.
<figure><img src="image_90.png" /><figcaption>FIGURE 1.—BRITISH INDIA AND NATIVE STATES</figcaption></figure>
* Level of aggregation: Indian districts.

* Treatment: district ∈ British Empire or native state in 1947.

* Main issue: no income or consumption data at district level.

* Main outcomes:

* Agricultural investment and productivity (1956–1987).

* Public goods: schools, health centers, roads (1981–1991).

* Development outcomes: literacy, infant mortality, poverty, inequality (1960s–1990s).

* Geographic controls: latitude, rainfall, coast, soil type.
$$y_i = \alpha + \beta \text{Brit}_i + \gamma \mathbf{X}_i + \varepsilon_i$$
$$y_i = \alpha + \beta \text{Brit}_i + \gamma X_i + \varepsilon_i$$

* Main issue: selection.

* British annexed most agriculturally productive areas.

* Estimates biased upward.
* Instrumental variable strategy.

* 1848–1856: annexation rule.

* Annex state when ruler dies without natural heir.
* Instrumental variable strategy.

* 1848–1856: annexation rule.

* Annex state when ruler dies without natural heir.

* Variable and sample:

* *Lapse*: indicator for native state not annexed before 1848 and ruler died without heir 1848–1856.

* Sample: native states never annexed + annexed due to lapse after 1847 + annexed by other means after 1847.
* Instrumental variable strategy.

* 1848–1856: annexation rule.

* Annex state when ruler dies without natural heir.

* Variable and sample:

* Lapse: indicator for native state not annexed before 1848 and ruler died without heir 1848–1856.

* Sample: native states never annexed + annexed due to lapse after 1847 + annexed by other means after 1847.

* First stage:

$$ \text{Brit}_i = \pi_0 + \pi_1 \text{Lapse}_i + \pi_2 X_i + u_i $$
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Brit</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Ruler died without natural heir in 1848–1856</td><td>0.682***<br>[0.159]</td><td>0.673***<br>[0.155]</td><td>0.669***<br>[0.162]</td><td>0.953***<br>[0.176]</td></tr><tr><td>Ruler died without heir</td><td></td><td></td><td></td><td>-0.231*<br>[0.126]</td></tr><tr><td>Ruler died in 1848–1856</td><td></td><td></td><td></td><td>-0.161<br>[0.101]</td></tr><tr><td>Geography</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Soils</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>R2</td><td>0.29</td><td>0.35</td><td>0.37</td><td>0.42</td></tr><tr><td>Districts</td><td>181</td><td>163</td><td>152</td><td>163</td></tr><tr><td>Native states</td><td>73</td><td>71</td><td>67</td><td>71</td></tr></tbody></table>

Standard errors clustered at the native state level.
* Agricultural investment and productivity: no correlation.
* Agricultural investment and productivity: no correlation.

* Public goods provision:

* Education: 37% fewer villages with middle schools.

* Health: 70% fewer villages with primary health centers.

* Roads: 46% fewer villages with access to good roads.
* Agricultural investment and productivity: no correlation.

* Public goods provision:

* Education: 37% fewer villages with middle schools.

* Health: 70% fewer villages with primary health centers.

* Roads: 46% fewer villages with access to good roads.

* Development:

* Poverty rates: 40% higher in 1990s.

* Infant mortality: 33% higher in 1990s.
* Post-colonial policies?

* Gap between areas narrowing over time.

* Differences in human capital already present in colonial period.
* Post-colonial policies?

* Gap between areas narrowing over time.

* Differences in human capital already present in colonial period.

* Colonial policies: extractive institutions under direct colonial rule.
* Post-colonial policies?

* Gap between areas narrowing over time.

* Differences in human capital already present in colonial period.

* Colonial policies: extractive institutions under direct colonial rule.

* Excessive taxation from the British, draining resources?
  No: native states extracted more.

* Landlord-based land revenue system in directly ruled area.

* Poor incentives for good colonial governance.
*Econometrica*, Vol. 78, No. 6 (November, 2010), 1863-1903

THE PERSISTENT EFFECTS OF PERU'S MINING *MITA*

BY MELISSA DELL¹
* Dell (2010)

* What are the long-term economic consequences of Spanish colonial rule in Peru?
* Dell (2010)

* What are the long-term economic consequences of Spanish colonial rule in Peru?

* Do districts under *mita* forced labor system (1573–1812) have worse economic outcomes today?
* 1573–1812 (240 years): 200 indigenous communities required to send 1/7 of adult males to silver and mercury mines.

* 3% of adult males in Peru were conscripted to the *mita* at any given point.

* Very precise geographical boundaries: forced within, exempt outside.

* Determinants of *mita* zone: distance to mines and elevation.
<figure><img src="image_91.png" /></figure>
* Level of aggregation: households in Peru districts around boundary.

* Treatment: household in district ∈ mita between 1573 and 1812.

* Main outcomes:

* Household consumption (2001).

* Health: children stunting (2005).

* Geographic controls: elevation, slope.
* Geographical regression discontinuity design.

* Compare very close (< 100 km) locations on either side of border.

* Main idea: identical along (un)observed characteristics.

$$y_{id} = \alpha + \beta mita_{d} + \gamma X_{id} + f(location_{d}) + \varepsilon_{id}$$
* Geographical regression discontinuity design.

* Compare very close (< 100 km) locations on either side of border.

* Main idea: identical along (un)observed characteristics.

$$y_{id} = \alpha + \beta mita_{d} + \gamma X_{id} + f(location_{d}) + \varepsilon_{id}$$

* Identical geography across border.

* Colonial outcomes similar across border.
* Household consumption: 25% lower in mita districts.

* Health: stunting 6pp more prevalent in mita districts.
Main Results

<figure><img src="image_92.png" /><figcaption>(a) Consumption (2001)</figcaption></figure>

<figure><img src="image_93.png" /><figcaption>(b) Stunting (2005)</figcaption></figure>
* Land tenure and labor systems:

* Spanish limited large haciendas in mita districts.

* Less haciendas even in 1940.
* Land tenure and labor systems:

* Spanish limited large haciendas in *mita* districts.

* Less haciendas even in 1940.

* Public goods.

* *Mita* lowered access to education historically, though not today.

* *Mita* districts less integrated into road network.

* Large landowners in non-*mita* districts have more secured property rights → incentives for public goods provision.
* Land tenure and labor systems:

* Spanish limited large haciendas in mita districts.

* Less haciendas even in 1940.

* Public goods.

* *Mita* lowered access to education historically, though not today.

* *Mita* districts less integrated into road network.

* Large landowners in non-*mita* districts have more secured property rights → incentives for public goods provision.

* Agricultural market participation.

* Lower access to markets, reducing household income.
<figure><img src="image_94.png" /><figcaption>(c) Haciendas (1689)</figcaption></figure>

<figure><img src="image_95.png" /><figcaption>(e) Haciendas (1940)</figcaption></figure>
<figure><img src="image_96.png" /><figcaption>(f) Education (1876)</figcaption></figure>

<figure><img src="image_97.png" /><figcaption>(g) Road Density (2006)</figcaption></figure>
<figure><img src="image_98.png" /></figure>
# The Development Effects of the Extractive Colonial Economy: The Dutch Cultivation System in Java

MELISSA DELL

Harvard University

and

BENJAMIN A. OLKEN

MIT
* Della and Olken (2020)

* What are the long-term economic consequences of Dutch colonial rule in Java?
* Della and Olken (2020)

* What are the long-term economic consequences of Dutch colonial rule in Java?

* Did places subjected to more intensive colonial extraction through Dutch Cultivation System have different development trajectories?
* 1830s–1870s: peasants along northern coast forced to cultivate sugar (25% of Javanese native population).
* 1830s–1870s: peasants along northern coast forced to cultivate sugar (25% of Javanese native population).

* Vast reorganization of economic structures:

* Before Cultivation System: Java heavily specialized in rice cultivation for local consumption.

* Dutch built 94 water-powered sugar factories to process cane into sugar.

* Sugar had to be grown nearby (4–7 km around factory).
Historical Context: the Dutch Cultivation System

<figure><img src="image_99.png" /></figure>
* Level of aggregation: villages in Java.

* Treatment: village close to historical sugar factory.

* Main outcomes (1980 census, 2001–2011 household surveys):

* Economic structure.

* Population density.

* Household consumption.
* Location of historical factories not random.

* Determinants of factory implantation:

* Land suitability for growing sugar.

* Proximity to river.

* Proximity to routes, population, firewood.

* Sufficient spacing between factories.

⇒ Many potential factory configurations.
* Location of historical factories not random.

* Determinants of factory implantation:

* Land suitability for growing sugar.

* Proximity to river.

* Proximity to routes, population, firewood.

* Sufficient spacing between factories.

⇒ Many potential factory configurations.

* Build a set of counterfactual factory locations.

* Counterfactual locations have similar characteristics as actual.
Counterfactual Factory Locations

<figure><img src="image_100.png" /></figure>
$$y_v = \alpha + \beta \text{Factory}_v + \gamma X_v + \text{fact}_v + \varepsilon_v$$

* Factory_v: indicator for factory close (< 10 km).

* X_v: geographical controls.

* fact_v: nearest factory FE = compare villages same factory.
$$y_v = \alpha + \beta \text{Factory}_v + \gamma X_v + \text{fact}_v + \varepsilon_v$$

* Factory_v: indicator for factory close (< 10 km).

* X_v: geographical controls.

* fact_v: nearest factory FE = compare villages same factory.

* Estimate separately for both actual and 1000 counterfactual factory locations.

* Coefficient of interest: difference between coefficient on actual factory vs average of 1,000 counterfactual factories.

* Focus on colonial factories without modern sugar factories nearby.
In places without modern factories, but within 10km of historical factory:

* Males aged 18–55 in 2001–2011 are:

* 18pp less likely to work in agriculture.

* 6pp more likely to work in industry.

* 9pp more likely to work in retail.

* Villages are more densely populated today.

* Household have higher consumption today.
# Main Results: Agriculture

<figure><img src="image_101.png" /><figcaption>(b) Agriculture (Census 1980)</figcaption></figure>

<figure><img src="image_102.png" /></figure>
<figure><img src="image_103.png" /><figcaption>(d) Manufacturing (Census 1980)</figcaption></figure>

<figure><img src="image_104.png" /></figure>
Main Results: Retail

<figure><img src="image_105.png" /><figcaption>(f) Retail (Census 1980)</figcaption></figure>

<figure><img src="image_106.png" /></figure>
# Main Results: Population Density

<figure><img src="image_107.png" /><figcaption>(h) Log Pop. Density (PODES 1980)</figcaption></figure>

<figure><img src="image_108.png" /></figure>
* Input-output linkages.

* Original source of sugar disappeared.

* Downstream centers persisted near historical factories.
* Input-output linkages.

* Original source of sugar disappeared.

* Downstream centers persisted near historical factories.

* Infrastructure investments.

* Road infrastructures built to support Cultivation System.

* Better roads and railroads network near historical factories today.
* Input-output linkages.

* Original source of sugar disappeared.

* Downstream centers persisted near historical factories.

* Infrastructure investments.

* Road infrastructures built to support Cultivation System.

* Better roads and railroads network near historical factories today.

* Human capital accumulation.

* More industrialized and connected ⇒ access to public goods.

* More electricity and high schools near historical factories in 1980.

* Better education outcomes near historical factories today.
<figure><img src="image_109.png" /><figcaption>(b) Colonial Railroad Density (1900)</figcaption></figure>

<figure><img src="image_110.png" /></figure>
<figure><img src="image_111.png" /><figcaption>(f) Railroad Density (2017)</figcaption></figure>

<figure><img src="image_112.png" /></figure>
<figure><img src="image_113.png" /><figcaption>(b) High Schools (PODES 1980)</figcaption></figure>

<figure><img src="image_114.png" /></figure>
<figure><img src="image_115.png" /><figcaption>(d) Distance to Subdistrict Capital
(2011 PODES)</figcaption></figure>

<figure><img src="image_116.png" /></figure>
(a) Years Education (2000 Census)

<figure><img src="image_117.png" /></figure>

(b) Years Education by Cohort

<figure><img src="image_118.png" /></figure>
(c) Primary (2000 Census)

<figure><img src="image_119.png" /></figure>

(d) Primary by Cohort

<figure><img src="image_120.png" /></figure>
<figure><img src="image_121.png" /><figcaption>(f) High School by Cohort</figcaption></figure>

<figure><img src="image_122.png" /></figure>
Long-term economic impact of colonial rule?
What Have We Learned?

* Credible link between current development and colonial institutions.
What Have We Learned?

* Credible link between current development and colonial institutions.

* AJR (2001): persistence of institutions.
What Have We Learned?

* Credible link between current development and colonial institutions.

* AJR (2001): persistence of institutions.

* Iyer (2010): convergence after colonial institutions disappear.
# What Have We Learned?

* Credible link between current development and colonial institutions.

* AJR (2001): persistence of institutions.

* Iyer (2010): convergence after colonial institutions disappear.

* Dell (2010): persistence through concentration of wealth and power
  ⇒ public goods provision.
# What Have We Learned?

* Credible link between current development and colonial institutions.

* AJR (2001): persistence of institutions.

* Iyer (2010): convergence after colonial institutions disappear.

* Dell (2010): persistence through concentration of wealth and power
  ⇒ public goods provision.

* Dell and Olken (2020): if colonial institutions created manufacturing and infrastructures locally ⇒ fosters industrialization in long run.
* More credible identification: very local answer.

⇒ low external validity.
# What Have We Learned?

* More credible identification: very local answer.

⇒ low external validity.

* No simple answer: depends on details and context.
# Long-term economic consequences of “colonial” institutions within Western Europe?
Log GDP per Capita and Rule of Law across Colonial Status in 2016

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

Source: V-Dem 9, GeoDist, 160 countries.
Log GDP per Capita and Rule of Law across Colonial Status in 2016

<figure><img src="image_124.png" /></figure>

Source: V-Dem 9, GeoDist, 160 countries.
Log GDP per Capita and Rule of Law across Colonial Status in 2016

<figure><img src="image_125.png" /></figure>

Source: V-Dem 9, GeoDist, 160 countries.
* From AJR 2001: extractive colonial institutions (in former European colonies) ⇒ persistence of weak institutions ⇒ lower long-run economic growth.

* But unwarranted cross-country comparisons, and unclear mechanisms.
* From AJR 2001: extractive colonial institutions (in former European colonies) ⇒ persistence of weak institutions ⇒ lower long-run economic growth.

* But unwarranted cross-country comparisons, and unclear mechanisms.

* Within-country studies show under which conditions extractive colonial institutions are detrimental:

* British rule in India (Iyer 2010).

* Spanish rule in Peru (Dell 2010).

* Dutch rule in Java (Dell and Olken 2020).
* From AJR 2001: extractive colonial institutions (in former European colonies) ⇒ persistence of weak institutions ⇒ lower long-run economic growth.

* But unwarranted cross-country comparisons, and unclear mechanisms.

* Within-country studies show under which conditions extractive colonial institutions are detrimental:

* British rule in India (Iyer 2010).

* Spanish rule in Peru (Dell 2010).

* Dutch rule in Java (Dell and Olken 2020).

* But how externally valid? Is it true even in Western Europe?
American Economic Review 101 (December 2011): 3286–3307

http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.7.3286

# The Consequences of Radical Reform:
The French Revolution<sup>†</sup>

By DARON ACEMOGLU, DAVIDE CANTONI, SIMON JOHNSON, AND JAMES A. ROBINSON<sup>‡</sup>
* Acemoglu et al. (2011)

* What are the economic consequences of externally imposed institutions?

* Were French institutional reforms early 19th century in Germany detrimental for long-run economic growth?
* Acemoglu et al. (2011)

* What are the economic consequences of externally imposed institutions?

* Were French institutional reforms early 19th century in Germany detrimental for long-run economic growth?

* Were restrictions to trade and labor entry by small powerful groups an impediment for European growth during the 19th century?
* Before the French revolution, Europe (incl. Germany) dominated by 2 types of oligarchies:

* Landed nobility in agriculture.

* Urban-based oligarchy controlling commerce and occupations.

* These groups restricted entry and adoption of new technologies.
* French invasions:

* 1792-1795: the French army takes control of Belgium, Netherlands, Switzerland, and Rhineland.

* 1801-1810: Napoléon takes control of Northern Germany, Saxony, Westphalia, Frankfurt...
* French invasions:

* 1792-1795: the French army takes control of Belgium, Netherlands, Switzerland, and Rhineland.

* 1801-1810: Napoléon takes control of Northern Germany, Saxony, Westphalia, Frankfurt...

* Institutional changes in Rhineland:

* 1795-1798: abolition of seigneurial regime and guilds, creation of commercial courts, imposition of civil code.

* After collapse in 1815: Rhineland keeps reforms, the rest reverts back.
* Level of aggregation: 19 preunitary polities in Germany.

* Treatment: number of years in 1792–1815 under French occupation.

* Main outcome: urbanization rate (population in cities of 5,000+) 1700–1900 as proxy for economic prosperity.

* Other outcome: sectoral shares, index of reforms (number of years since reform / 4).

* Controls:
<table><caption>TABLE 1—TERRITORIES AND REFORMS</caption><thead><tr><th>Territory</th><th>Years of French presence (1)</th><th>Civil code (2)</th><th>Abolition of serfdom (3)</th><th>Agrarian reform (4)</th><th>Abolition of guilds (5)</th><th>Reforms index as of 1850 (6)</th><th>Reforms index as of 1900 (7)</th><th>Pop. weights (1750) (8)</th></tr></thead><tbody><tr><td colspan="9"><strong>Panel A. Treatment</strong></td></tr><tr><td>Rhineland (Prussia)</td><td>19</td><td>1802</td><td>1798</td><td>1804</td><td>1795</td><td>50.25</td><td>100.25</td><td>1,439</td></tr><tr><td>Palatinate (Bavaria)</td><td>19</td><td>1802</td><td>1798</td><td>1804</td><td>1795</td><td>50.25</td><td>100.25</td><td>239</td></tr><tr><td>Mark/Ruhr (Prussia)</td><td>6</td><td>1810-15, 1900</td><td>1808</td><td>1825</td><td>1809</td><td>28.25</td><td>65.75</td><td>150</td></tr><tr><td>Westphalia (Prussia)</td><td>6</td><td>1810-15, 1900</td><td>1808</td><td>1825</td><td>1809</td><td>28.25</td><td>65.75</td><td>529</td></tr><tr><td>Brunswick</td><td>6</td><td>1808-14, 1900</td><td>1808-18, 1834</td><td>1809-18, 1834</td><td>1808-15, 1864</td><td>16</td><td>50</td><td>155</td></tr><tr><td>Province of Saxony (Prussia)</td><td>6</td><td>1808-15, 1900</td><td>1808</td><td>1809</td><td>1809</td><td>32.75</td><td>70.25</td><td>763</td></tr><tr><td>Hessen-Kassel</td><td>6</td><td>1808-14, 1900</td><td>1808-14, 1832</td><td>1809-14, 1832</td><td>1808-16, 1869</td><td>15.25</td><td>48</td><td>294</td></tr><tr><td>Hanover</td><td>3</td><td>1808-13, 1900</td><td>1808-14, 1833</td><td>1809-14, 1833</td><td>1808-15, 1869</td><td>14.25</td><td>47</td><td>1,090</td></tr><tr><td>Average</td><td>9.98</td><td></td><td></td><td></td><td></td><td>32.41</td><td>72.88</td><td></td></tr><tr><td colspan="9"><strong>Panel B. Control</strong></td></tr><tr><td>Baden</td><td>0</td><td>1810</td><td>1783</td><td>1820</td><td>1862</td><td>34.25</td><td>81.25</td><td>609</td></tr><tr><td>Bavaria, southern half</td><td>0</td><td>1900</td><td>1808</td><td>1826</td><td>1868</td><td>16.5</td><td>49.5</td><td>1,163</td></tr><tr><td>Hessen-Darmstadt</td><td>0</td><td>1900</td><td>1811</td><td>1816</td><td>1866</td><td>18.25</td><td>51.75</td><td>264</td></tr><tr><td>Saxony</td><td>0</td><td>1865</td><td>1832</td><td>1832</td><td>1862</td><td>9</td><td>52.25</td><td>1,020</td></tr><tr><td>Württemberg</td><td>0</td><td>1900</td><td>1817</td><td>1836</td><td>1862</td><td>11.75</td><td>46.25</td><td>925</td></tr><tr><td>Average</td><td>0</td><td></td><td></td><td></td><td></td><td>16.31</td><td>54.46</td><td></td></tr><tr><td colspan="9"><strong>Panel C. Control (east of the Elbe)</strong></td></tr><tr><td>Brandenburg (Prussia)</td><td>0</td><td>1900</td><td>1811</td><td>1821</td><td>1810</td><td>27</td><td>64.5</td><td>797</td></tr><tr><td>East Prussia (Prussia)</td><td>0</td><td>1900</td><td>1811</td><td>1821</td><td>1810</td><td>27</td><td>64.5</td><td>554</td></tr><tr><td>Pomerania (Prussia)</td><td>0</td><td>1900</td><td>1811</td><td>1821</td><td>1810</td><td>27</td><td>64.5</td><td>342</td></tr><tr><td>Silesia (Prussia)</td><td>0</td><td>1900</td><td>1811</td><td>1821</td><td>1810</td><td>27</td><td>64.5</td><td>1,053</td></tr><tr><td>Mecklenburg-Schwerin</td><td>0</td><td>1900</td><td>1820</td><td>1862</td><td>1869</td><td>7.5</td><td>37.25</td><td>217</td></tr><tr><td>Schleswig-Holstein</td><td>0</td><td>1900</td><td>1805</td><td>1805</td><td>1867</td><td>22.5</td><td>55.75</td><td>541</td></tr><tr><td>Average</td><td>0</td><td></td><td></td><td></td><td></td><td>25.1</td><td>61.46</td><td></td></tr></tbody></table>
<figure><img src="image_126.png" /><figcaption>FIGURE 1. REFORMS INDEX, BY TREATMENT GROUP</figcaption></figure>
<figure><img src="image_127.png" /><figcaption>FIGURE 2A. URBANIZATION RATES, BY TREATMENT GROUP</figcaption></figure>
<figure><img src="image_128.png" /><figcaption>FIGURE 2B. URBANIZATION RATES, BY TREATMENT GROUP</figcaption></figure>
$$ (1) \quad u_{jt} = d_t + \delta_j + \sum_{\tau \in T^{pre}} \alpha_{\tau} \times d_{\tau} \times I_j + \sum_{\tau \in T^{post}} \alpha_{\tau} \times d_{\tau} \times I_j + \mathbf{X}_{jt}' \times \mathbf{\gamma} + \varepsilon_{jt}, $$

* d: year fixed effects.

* l: number of years treated.

* α: coefficients of interest.

* Year-specific difference-in-difference estimates.

* Clustering: polity level.
<table><caption>TABLE 3—URBANIZATION IN GERMANY</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent variable: urbanization rate</th></tr><tr><th colspan="2">West of the Elbe</th><th colspan="2">All</th></tr><tr><th>Weighted (1)</th><th>Unweighted (2)</th><th>Weighted (3)</th><th>Unweighted (4)</th></tr></thead><tbody><tr><td>Years French presence × 1750</td><td>-0.491<br>[0.249]</td><td>-0.252<br>[0.172]</td><td>-0.488<br>[0.235]</td><td>-0.197<br>[0.164]</td></tr><tr><td>Years French presence × 1800</td><td>-0.247<br>[0.225]</td><td>-0.0425<br>[0.153]</td><td>-0.268<br>[0.227]</td><td>-0.0471<br>[0.178]</td></tr><tr><td>Years French presence × 1850</td><td>-0.160<br>[0.250]</td><td>0.0332<br>[0.153]</td><td>-0.221<br>[0.249]</td><td>-0.0235<br>[0.181]</td></tr><tr><td>Years French presence × 1875</td><td>0.402<br>[0.326]</td><td>0.354<br>[0.295]</td><td>0.266<br>[0.303]</td><td>0.252<br>[0.299]</td></tr><tr><td>Years French presence × 1900</td><td>0.634<br>[0.408]</td><td>0.529<br>[0.401]</td><td>0.503<br>[0.376]</td><td>0.506<br>[0.423]</td></tr><tr><td>Observations</td><td>74</td><td>74</td><td>109</td><td>109</td></tr><tr><td>Number of states</td><td>13</td><td>13</td><td>19</td><td>19</td></tr><tr><td>p-value for joint significance after 1800</td><td>0.0532</td><td>0.463</td><td>0.0205</td><td>0.214</td></tr></tbody></table>

*Notes:* All regressions have full set of territory and year dummies. Robust standard errors clustered by territory. Weighted regressions are weighted by territories' total population in 1750.

No negative destructive implications, even positive ones.
<table><caption>TABLE 5—OCCUPATIONAL SHARES IN GERMANY</caption><thead><tr><th rowspan="3"></th><th colspan="3">Dependent variable: share of population employed in agriculture</th><th colspan="3">Dependent variable: share of population employed in industry</th></tr><tr><th colspan="2">West of the Elbe</th><th>All</th><th colspan="2">West of the Elbe</th><th>All</th></tr><tr><th>Weighted (1)</th><th>Unweighted (2)</th><th>Weighted (3)</th><th>Weighted (4)</th><th>Unweighted (5)</th><th>Weighted (6)</th></tr></thead><tbody><tr><td>Years French presence, 1849</td><td>-0.430<br>[0.468]</td><td>-0.411<br>[0.460]</td><td>-0.508<br>[0.346]</td><td>0.055<br>[0.376]</td><td>0.061<br>[0.342]</td><td>0.374<br>[0.369]</td></tr><tr><td>Years French presence, 1882</td><td>-0.450<br>[0.285]</td><td>-0.486<br>[0.244]</td><td>-0.585<br>[0.253]</td><td>0.420<br>[0.256]</td><td>0.386<br>[0.240]</td><td>0.594<br>[0.267]</td></tr><tr><td>Years French presence, 1895</td><td>-0.570<br>[0.266]</td><td>-0.601<br>[0.242]</td><td>-0.658<br>[0.182]</td><td>0.472<br>[0.248]</td><td>0.449<br>[0.231]</td><td>0.640<br>[0.222]</td></tr><tr><td>Years French presence, 1907</td><td>-0.554<br>[0.281]</td><td>-0.585<br>[0.264]</td><td>-0.724<br>[0.237]</td><td>0.350<br>[0.284]</td><td>0.321<br>[0.251]</td><td>0.570<br>[0.237]</td></tr></tbody></table>

Notes: Each cell corresponds to one cross-sectional regression. District level data. Robust standard errors clustered at the state level. All regressions weighted by the districts' total population in 1849. Number of observations (baseline/west of Elbe): 39/23 (1849), 62/44 (other years).

Faster structural transformation.
$$ (3) \qquad R_{jt} = d_t + \delta_j + \psi \times t \times T_{t>1800} \times I_j + \eta_{jt}, $$

* R: reform index.

* Linear time trend post reform to account for mechanical increase in reform index over time.

* Use it as first stage in 2SLS approach.

$$ (4) \qquad u_{jt} = d_t + \delta_j + \phi \times R_{jt} + v_{jt}, $$
<table><caption>TABLE 6—URBANIZATION IN GERMANY, IMPACT OF REFORMS</caption><thead><tr><th rowspan="3"></th><th colspan="6">Dependent variable: urbanization rate</th></tr><tr><th colspan="3">West of the Elbe</th><th colspan="3">All</th></tr><tr><th>Weighted<br>(1)</th><th>Weighted,<br>overid<br>(2)</th><th>Unweighted<br>(3)</th><th>Weighted<br>(4)</th><th>Unweighted<br>(5)</th></tr></thead><tbody><tr><td colspan="7"><em>Panel A. OLS estimation</em></td></tr><tr><td>Reforms index</td><td>0.281<br>[0.114]</td><td>0.281<br>[0.114]</td><td>0.220<br>[0.122]</td><td>0.268<br>[0.110]</td><td>0.191<br>[0.105]</td></tr><tr><td colspan="7"><em>Panel B. First stage</em></td></tr><tr><td>French presence × post1800<br>× trend</td><td>1.166<br>[0.107]</td><td></td><td>1.116<br>[0.143]</td><td>1.006<br>[0.108]</td><td>0.960<br>[0.145]</td></tr><tr><td>F-statistic excluded instruments</td><td>119.7</td><td>121.6</td><td>61.85</td><td>87.57</td><td>43.71</td></tr><tr><td>p-value F-statistic</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td colspan="7"><em>Panel C. 2SLS estimation</em></td></tr><tr><td>Reforms index</td><td>0.291<br>[0.102]</td><td>0.321<br>[0.112]</td><td>0.204<br>[0.124]</td><td>0.284<br>[0.112]</td><td>0.193<br>[0.143]</td></tr><tr><td>Observations</td><td>74</td><td>74</td><td>69</td><td>109</td><td>109</td></tr><tr><td>Number of states</td><td>13</td><td>13</td><td>12</td><td>19</td><td>19</td></tr><tr><td>p-value overidentified test</td><td colspan="2">0.328</td><td colspan="3"></td></tr></tbody></table>

Notes: All regressions have full set of territory and year dummies. Robust standard errors clustered by territory. Weighted regressions are weighted by territories' total population in 1750. The overidentified regression in column 2 uses a full set of interactions of “Years of French presence” and year dummies as excluded instruments.

Positive effects on reforms and growth through induced reforms (little bias in OLS).
<figure><img src="image_129.png" /></figure>

# The economic consequences of the Spanish Reconquest: the long-term effects of Medieval conquest and colonization

Daniel Oto-Peralías¹ · Diego Romero-Ávila²
* Oto-Peralias and Romero-Avila (2016)

* What were the economic consequences of Spanish (re)-colonization in Spain?

* Was the extractive nature of Spanish colonization detrimental to long-run growth?
* 711: Spain invaded by Muslims (al-Andalus).

* Fosters positive economic and cultural development.

* Reconquista from the Christians that lasted until 1492.

* Nature of colonization: repopulation of new lands differed with speed of reconquista:
  * Where slow process: more complete and balanced population, with participation of individual settlers and preservation of Muslim agricultural technology.
  * Where fast process: insufficient resources, administrative difficulties to control: favors nobility and military orders to organize and defend land, dispersed settlements in large jurisdictional areas.
<figure><img src="image_130.png" /><figcaption>Fig. 1 The Spanish Reconquest (711–1492)</figcaption></figure>
* Level of aggregation: 50 Spanish provinces (45 never occupied by Muslims).

* Treatment: rate of Reconquest (total area of Reconquest / years of stage) in km2 per year, based on 16 Reconquest stages.

* Main outcomes: GDP per capita in 2005.

* Controls: array of climatic, geographic, topographic, and historical controls (years since agriculture, Roman roads density, urban density 800, Muslim domination years, rainfall, soil quality, land suitability, distance to coast,...).
$$Y_i = \alpha + \beta_1 \cdot \text{Reconquest}_i + \beta_2 \cdot X_i + \omega_i \quad (1)$$

* Simple OLS regression at province level.

* Issue: “Column 1 in Table 2 includes in the same specification all the controls that are individually significant at the 10 % level or better. This is our paper’s baseline specification.”

* Process correlated with North-South gradient: excludes Basque country, Madrid, Barcelona, control for latitude, tune a municipality-level analysis with province fixed effects on 8,000 municipalities.

* Territories conquested before might have different characteristics: falsification test with pre-Reconquest development.
<figure><img src="image_131.png" /><figcaption>Fig. 2 Conditional relationship between current GDP per capita and rate of Reconquest</figcaption></figure>
# Main Results

<table><caption>Table 3 Municipality-level analysis: province fixed-effects regressions</caption><thead><tr><th rowspan="2"></th><th colspan="3">Dependent variable</th><th colspan="3">Average number of vehicles per household</th><th colspan="3">Labor force activity rate</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td colspan="10"><strong>Panel A:</strong></td></tr><tr><td>Rate of Reconquest</td><td>-0.157**<br>(0.071)</td><td></td><td></td><td>-0.004*<br>(0.002)</td><td></td><td></td><td>-0.139**<br>(0.053)</td><td></td><td></td></tr><tr><td>High rate of<br>Reconquest<br>(> provincial<br>average)</td><td></td><td>-0.987**<br>(0.374)</td><td></td><td></td><td>-0.039***<br>(0.01)</td><td></td><td></td><td>-0.641**<br>(0.24)</td><td></td></tr><tr><td>High rate of<br>Reconquest<br>(> 1.25*provincial<br>average)</td><td></td><td></td><td>-1.467***<br>(0.382)</td><td></td><td></td><td>-0.043***<br>(0.013)</td><td></td><td></td><td>-0.789***<br>(0.219)</td></tr><tr><td>Geo-climatic<br>controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Province dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standardized beta</td><td>-0.055</td><td>-0.033</td><td>-0.039</td><td>-0.075</td><td>-0.069</td><td>-0.062</td><td>-0.103</td><td>-0.045</td><td>-0.045</td></tr><tr><td>R<sup>2</sup></td><td>0.60</td><td>0.60</td><td>0.60</td><td>0.53</td><td>0.53</td><td>0.53</td><td>0.24</td><td>0.24</td><td>0.24</td></tr><tr><td>Number of<br>observations</td><td>7,590</td><td>7,590</td><td>7,590</td><td>7,590</td><td>7,590</td><td>7,590</td><td>7,590</td><td>7,590</td><td>7,590</td></tr><tr><td colspan="10"><strong>Panel B:</strong></td></tr><tr><td>Rate of Reconquest</td><td>-0.197**<br>(0.07)</td><td></td><td></td><td>-0.004**<br>(0.001)</td><td></td><td></td><td>-0.171***<br>(0.04)</td><td></td><td></td></tr></tbody></table>
# Main Results

<table><caption>Table 4 Falsification test: the effect of the Reconquest on pre-Reconquest development</caption><thead><tr><th>Dependent variable</th><th>City population in 800</th><th>Density of urban population in 800</th><th>Years since transition to agriculture</th><th>Ancient settlements over surface area</th><th>Roman roads density</th><th>Roman roads density: Main roads</th><th>Coinage of imperial Roman coins over surface area</th><th>Roman villas over surface area</th><th>Number of bishoprics circa 600 over surface area</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td colspan="10">Panel A: Conditional relationship between rate of Reconquest and pre-Reconquest economic development</td></tr><tr><td>Rate of Reconquest</td><td>1.826 (1.197)</td><td>0.13 (0.087)</td><td>-1.943 (1.491)</td><td>0.021 (0.022)</td><td>0.449 (0.292)</td><td>0.158 (0.2)</td><td>0.00 (0.002)</td><td>0.002 (0.01)</td><td>-0.001 (0.002)</td></tr><tr><td>Rainfall</td><td>0.024 (0.02)</td><td>0.002 (0.001)</td><td>-0.025 (0.032)</td><td>0.00 (0.001)</td><td>0.046*** (0.01)</td><td>0.002 (0.007)</td><td>0.00 (0.00)</td><td>0.001** (0.00)</td><td>0.0002** (0.00)</td></tr><tr><td>Soil quality</td><td>18.271 (14.544)</td><td>1.289 (1.042)</td><td>-26.626 (40.256)</td><td>2.064** (0.939)</td><td>23.158 (15.205)</td><td>7.886 (7.24)</td><td>0.121 (0.088)</td><td>0.825*** (0.209)</td><td>0.144 (0.098)</td></tr><tr><td>Ruggedness</td><td>-12.723 (11.852)</td><td>-0.762 (0.869)</td><td>-5.768 (15.967)</td><td>1.539** (0.731)</td><td>-17.708*** (6.234)</td><td>1.193 (4.111)</td><td>0.064 (0.061)</td><td>0.203 (0.223)</td><td>0.025 (0.058)</td></tr><tr><td>Mediterranean Sea</td><td>1.851 (5.439)</td><td>0.113 (0.411)</td><td>6.298 (15.701)</td><td>-0.294 (0.512)</td><td>17.48*** (6.136)</td><td>6.127 (3.805)</td><td>-0.011 (0.047)</td><td>-0.022 (0.16)</td><td>0.114** (0.055)</td></tr><tr><td>Cantabrian Sea</td><td>-2.304 (4.294)</td><td>-0.159 (0.313)</td><td>2.944 (13.451)</td><td>-0.136 (0.361)</td><td>-17.266*** (5.309)</td><td>-2.328 (2.187)</td><td>-0.008 (0.029)</td><td>-0.232** (0.104)</td><td>0.06* (0.03)</td></tr><tr><td>Standardized beta</td><td>0.426 (0.18)</td><td>0.420 (0.17)</td><td>-0.318 (0.14)</td><td>0.129 (0.40)</td><td>0.199 (0.37)</td><td>0.130 (0.25)</td><td>0.000 (0.11)</td><td>0.039 (0.27)</td><td>-0.055 (0.37)</td></tr><tr><td>Number of observations</td><td>45</td><td>45</td><td>45</td><td>43</td><td>45</td><td>45</td><td>45</td><td>43</td><td>45</td></tr></tbody></table>
<table><caption>Table 6 The timing of the effect of the Reconquest: regression results</caption><thead><tr><th colspan="7">Dependent variable is relative economic development</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Rate of Reconquest x D<sub>1500</sub></td><td>-5.909<br>(3.714)</td><td>-6.235<br>(3.976)</td><td>-6.003<br>(3.775)</td><td>-5.788<br>(3.581)</td><td>-5.977<br>(3.731)</td><td>-7.553*<br>(4.243)</td><td>-6.444*<br>(3.711)</td></tr><tr><td>Rate of Reconquest x D<sub>1600</sub></td><td>-3.066<br>(4.35)</td><td>-3.506<br>(4.553)</td><td>-3.107<br>(4.418)</td><td>-2.959<br>(4.221)</td><td>-3.316<br>(4.272)</td><td>-3.354<br>(4.75)</td><td>-2.543<br>(4.145)</td></tr><tr><td>Rate of Reconquest x D<sub>1700</sub></td><td>-2.969<br>(3.963)</td><td>-3.496<br>(4.092)</td><td>-2.994<br>(4.023)</td><td>-2.831<br>(3.794)</td><td>-3.078<br>(3.984)</td><td>-8.95**<br>(4.295)</td><td>-8.67**<br>(3.799)</td></tr><tr><td>Rate of Reconquest x D<sub>1800</sub></td><td>-4.852<br>(4.114)</td><td>-5.531<br>(4.296)</td><td>-4.779<br>(4.179)</td><td>-4.764<br>(4.07)</td><td>-4.843<br>(4.03)</td><td>-7.169*<br>(4.314)</td><td>-6.383*<br>(3.761)</td></tr><tr><td>Rate of Reconquest x D<sub>1860</sub></td><td>-8.772**<br>(4.219)</td><td>-9.015**<br>(4.41)</td><td>-8.807**<br>(4.283)</td><td>-8.694**<br>(4.199)</td><td>-8.985**<br>(4.272)</td><td>-9.558**<br>(4.605)</td><td>-8.992**<br>(4.209)</td></tr><tr><td>Rate of Reconquest x D<sub>1930</sub></td><td>-10.704**<br>(4.388)</td><td>-10.602**<br>(4.587)</td><td>-10.568**<br>(4.45)</td><td>-10.647**<br>(4.359)</td><td>-10.893**<br>(4.455)</td><td>-10.729**<br>(4.876)</td><td>-10.126**<br>(4.516)</td></tr><tr><td>Rate of Reconquest x D<sub>1971</sub></td><td>-11.427**<br>(4.432)</td><td>-11.345**<br>(4.632)</td><td>-11.333**<br>(4.498)</td><td>-11.37**<br>(4.405)</td><td>-11.65**<br>(4.523)</td><td>-11.749**<br>(4.938)</td><td>-11.222**<br>(4.623)</td></tr><tr><td>Rate of Reconquest x D<sub>2005</sub></td><td>-11.578**<br>(4.504)</td><td>-11.392**<br>(4.709)</td><td>-11.438**<br>(4.571)</td><td>-11.517**<br>(4.483)</td><td>-11.825**<br>(4.599)</td><td>-11.809**<br>(4.989)</td><td>-11.201**<br>(4.69)</td></tr></tbody></table>
* Rate of Reconquest affected who gained control over new territories: settlers or nobility and military.

* Rapid frontier expansion favors initial political equilibrium toward nobility, leading to concentration of political and economic power.

* This enabled the nobility to run de facto extractive institutions and exploit the peasantry through restrictions on land and grain transactions, labor contracts with restrictions on wages, land tenure systems.

* Generated high level of social and political inequality, no equal opportunity and property rights access, leading to failure to industrialize and adopt new technology.

* Use a 2SLS strategy (not in IV spirit) to assess explanatory power of each channel.
# Mechanisms

<table><caption>Table 7 Mechanisms at work</caption><thead><tr><th>The channel variable →</th><th>Percentage of landless workers 1797</th><th>Percentage of landless workers 1956</th><th>Percentage of population entities under seigneurial jurisdiction in 1787</th><th>Family types</th><th>Moorish ancestry</th><th>Market fragmentation (Road density in 1760)</th><th>Percentage of population entities under Church jurisdiction in 1787</th><th>Religiosity (Clerical population in 1797)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td colspan="9">Panel A: Second stage (Log GDP pc 2005 regressed on the predicted values of the channel variable)</td></tr><tr><td>The channel variable</td><td>-0.009***<br>(0.003)</td><td>-0.008***<br>(0.002)</td><td>-0.010**<br>(0.004)</td><td>25.689<br>(44.709)</td><td>0.014<br>(0.011)</td><td>-78.654<br>(158.623)</td><td>0.034**<br>(0.015)</td><td>-85.605<br>(64.005)</td></tr><tr><td>Geo-climatic controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standardized beta</td><td>-0.770</td><td>-0.694</td><td>-0.750</td><td>8.385</td><td>0.506</td><td>-4.057</td><td>1.687</td><td>-1.536</td></tr><tr><td colspan="9">Panel B: First stage (The channel variable regressed on rate of Reconquest)</td></tr><tr><td>Rate of Reconquest</td><td>1.683***<br>(0.402)</td><td>1.915***<br>(0.473)</td><td>1.58**<br>(0.696)</td><td>-0.001<br>(0.001)</td><td>-0.456<br>(0.277)</td><td>0.000<br>(0.000)</td><td>-0.461**<br>(0.181)</td><td>0.000<br>(0.000)</td></tr><tr><td>F-statistic</td><td>17.53</td><td>16.41</td><td>5.15</td><td>0.37</td><td>2.71</td><td>0.24</td><td>6.47</td><td>1.67</td></tr><tr><td>Partial R<sup>2</sup></td><td>0.327</td><td>0.355</td><td>0.116</td><td>0.006</td><td>0.077</td><td>0.006</td><td>0.074</td><td>0.077</td></tr><tr><td>Geo-climatic controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standardized beta</td><td>0.514</td><td>0.567</td><td>0.393</td><td>-0.084</td><td>-0.334</td><td>0.000</td><td>-0.231</td><td>0.000</td></tr><tr><td>R<sup>2</sup></td><td>0.70</td><td>0.68</td><td>0.36</td><td>0.78</td><td>0.60</td><td>0.47</td><td>0.64</td><td>0.61</td></tr><tr><td>Number of observations</td><td>45</td><td>45</td><td>45</td><td>45</td><td>43</td><td>45</td><td>45</td><td>45</td></tr></tbody></table>
<table><caption>Table 7 continued</caption><thead><tr><th>Channel variable →</th><th>Percentage of landless workers 1797</th><th>Percentage of landless workers 1956</th><th>Percentage of population entities under seigneurial jurisdiction in 1787</th><th>Family types</th><th>Moorish ancestry</th><th>Market fragmentation (Road density in 1760)</th><th>Percentage of population entities under Church jurisdiction in 1787</th><th>Religiosity (Clerical population in 1797)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td colspan="9">Panel C: OLS regressions of Log GDP pc 2005 on the channel variable</td></tr><tr><td>The channel variable</td><td>-0.004**<br>(0.002)</td><td>-0.006***<br>(0.001)</td><td>-0.003***<br>(0.001)</td><td>-0.178<br>(0.565)</td><td>0.005*<br>(0.003)</td><td>-1.814<br>(2.162)</td><td>0.003<br>(0.002)</td><td>-9.355<br>(7.905)</td></tr><tr><td>Geo-climatic controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standardized beta</td><td>-0.408</td><td>-0.631</td><td>-0.375</td><td>-0.066</td><td>0.213</td><td>-0.137</td><td>0.186</td><td>-0.215</td></tr><tr><td>R<sup>2</sup></td><td>0.72</td><td>0.80</td><td>0.74</td><td>0.63</td><td>0.82</td><td>0.64</td><td>0.65</td><td>0.65</td></tr><tr><td>Number of observations</td><td>45</td><td>45</td><td>45</td><td>45</td><td>43</td><td>45</td><td>45</td><td>45</td></tr></tbody></table>

Variables descriptions are provided in Table 9. All the estimations include the control set employed in the baseline specification (column 1, Table 2) and a constant term, which are omitted for space considerations. Robust standard errors are in parentheses. Small-sample correction for standard errors is applied in 2SLS regressions. The respective standardized beta coefficients are reported in the bottom part of each panel

\*, \*\* and \*\*\* denote statistical significance at the 10, 5 and 1 % level, respectively
<table><caption>Table 8 Outcomes indicators in the 1860s</caption><thead><tr><th>The outcome indicator in the 1860s →</th><th>Literacy rate (1)</th><th>School enrollment (2)</th><th>Infant mortality (3)</th><th>Life expectancy (4)</th><th>Percentage of electors (5)</th><th>Percentage of voters (6)</th><th>Crimes (7)</th><th>Convicts (8)</th></tr></thead><tbody><tr><td colspan="9">Panel A: 2SLS Results (The outcome indicator regressed on the predicted value of the percentage of landless workers in 1797)</td></tr><tr><td>Percentage of landless workers 1797</td><td>-0.32**<br>(0.141)</td><td>-0.002*<br>(0.001)</td><td>0.64<br>(0.64)</td><td>-0.15**<br>(0.064)</td><td>-0.001***<br>(0.00)</td><td>-0.001*<br>(0.00)</td><td>0.037***<br>(0.013)</td><td>0.03***<br>(0.01)</td></tr><tr><td>Geo-climatic controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standardized beta</td><td>-0.515</td><td>-0.398</td><td>0.229</td><td>-0.488</td><td>-0.549</td><td>-0.764</td><td>0.656</td><td>0.712</td></tr><tr><td colspan="9">Panel B: Reduced-form Effect (The outcome indicator regressed on rate of Reconquest)</td></tr><tr><td>Rate of Reconquest</td><td>-0.539***<br>(0.182)</td><td>-0.003**<br>(0.001)</td><td>1.078<br>(1.074)</td><td>-0.253**<br>(0.111)</td><td>-0.002***<br>(0.00)</td><td>-0.001*<br>(0.00)</td><td>0.062***<br>(0.022)</td><td>0.05***<br>(0.013)</td></tr><tr><td>Geo-climatic controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standardized beta</td><td>-0.316</td><td>-0.218</td><td>0.140</td><td>-0.299</td><td>-0.400</td><td>-0.278</td><td>0.400</td><td>0.432</td></tr><tr><td>R<sup>2</sup></td><td>0.77</td><td>0.74</td><td>0.58</td><td>0.66</td><td>0.64</td><td>0.51</td><td>0.57</td><td>0.63</td></tr><tr><td>Number of observations</td><td>45</td><td>45</td><td>45</td><td>45</td><td>45</td><td>45</td><td>45</td><td>45</td></tr></tbody></table>

Variables descriptions are provided in Table 9. All the estimations include the control set employed in the baseline specification (column 1, Table 2) and a constant term, which are omitted for space considerations. Robust standard errors are in parentheses. Small-sample correction for standard errors is applied in 2SLS regressions. The respective standardized beta coefficients are reported in the bottom part of each panel

\*, \*\* and \*\*\* denote statistical significance at the 10, 5 and 1% level, respectively
European Review of Economic History, 00, 1-23 © The Author(s) 2020. Published by Oxford University Press on behalf of the European Historical Economics Society. All rights reserved. For permissions, please e-mail: journals permissions@oup.com
doi:10.1093/creh/heaa021

Institutions and literacy rates: the legacy of
Napoleonic reforms in Italy

M. POSTIGLIOLA* AND M. ROTA**

* Sapienza University of Rome, Department MEMOTEF, via del Castro Laurenziano 9, 00161 Rome, michele.postigliola@uniroma1.it

** Sapienza University of Rome, Department MEMOTEF, via del Castro Laurenziano 9, 00161 Rome, mauro.rota@uniroma1.it
* Postigliola and Rota (2020)

* Were the Napoleonic reforms at the roots of the North-South divide in human capital in Italy?

* Were inclusive “colonial” institutions beneficial to long-run literacy?
* Two phases of French presence in Italy:

* 1796–1799: short lived dominance of military.

* 1799–1815: political control with territorial reorganization and reforms.

* French reforms in education:

* Supply of education: opening new primary schools, recruiting teachers, reviewing curriculum, secularizing the system (conditioned on administrative capacity).

* Demand of education: introduction of Code Napoléon promoted idea of social equality, greater protection of property rights and efforts to create inclusive society (eg abolition of guilds). All created economic opportunities which increased demand for education.

* Post-Napoleon pre-unitary reforms (1815–1861): pursued efforts but less marked, and in some locations control of clergy.
<figure><img src="image_132.png" /></figure>

Note: For definition and sources, see text.

<caption>Figure 1. Duration of French political control in Italy (1801–1815).</caption>
<figure><img src="image_133.png" /></figure>

Source: see text and Appendixes A1 and A2.1. Scatterplot is conditional to a set of controls used in the regressions below.

<caption>Figure 2. Schools per 1,000 inhabitants in 1814 ca. and French political control.</caption>
<table><caption>Table 1. Primary schools (public) per 1,000 inhabitants</caption><thead><tr><th></th><th>1790 ca.</th><th>1810-1814</th><th>1818-1822</th><th>1831-1835</th><th>1841-1844</th><th>1855-1858</th></tr></thead><tbody><tr><td>Kingdom of Sardinia</td><td></td><td>0.63<sup>a</sup></td><td></td><td></td><td></td><td>1.47</td></tr><tr><td>Lombardy</td><td>0.31</td><td>0.63</td><td></td><td></td><td></td><td></td></tr><tr><td>Lombardy (incl. Bergamo and Brescia)</td><td></td><td>0.91</td><td>1.18</td><td></td><td>1.57</td><td>1.56</td></tr><tr><td>Veneto</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Duchy of Parma and Piacenza</td><td>0.07</td><td>0.64</td><td></td><td>0.71</td><td>0.78</td><td>0.77</td></tr><tr><td>Duchy of Modena and Reggio<sup>b</sup></td><td>0.17</td><td>0.56</td><td></td><td></td><td></td><td></td></tr><tr><td>Granduchy of Tuscany<sup>c</sup></td><td>0.35</td><td>0.23<sup>d</sup></td><td></td><td></td><td>0.54<sup>d</sup></td><td></td></tr><tr><td>Papal States<sup>e</sup></td><td>0.12</td><td>0.51</td><td></td><td></td><td></td><td>0.68</td></tr><tr><td>Kingdom of Two Sicilies<sup>f</sup></td><td>0.03</td><td>0.55</td><td>0.50<sup>g</sup></td><td></td><td></td><td>0.40</td></tr><tr><td>Sicily</td><td></td><td></td><td>0.22<sup>h</sup></td><td></td><td></td><td>0.15</td></tr></tbody></table>

Notes: a = only Novara; b = excluding Garfagnana; c = Massa is not included; d = number of teachers; e = Legazioni of Romagna; f = mainland; g = Sicily included; h = excluding cities of Palermo, Catania, and Caltanissetta. Sources: see Appendix A1.
* Level of aggregation: 59 provinces.

* Treatment: months of the political influence of Napoléon.

* Main outcomes: male and female literacy rates in 1861 census.

* Controls: literacy before 1801, institutional profile pre-unity, urbanization before French rule, geographical controls (access to see, trade openness, rainfall).
<figure><img src="image_134.png" /></figure>

Source: Our computation on 1861 census data. For definition, see text.

<caption>Figure 3. Literacy rates at the time of unification (1861).</caption>
* Simple OLS regression with controls.

* Issue: effectiveness of reforms most pronounced in areas with pre-Napoléon institutional reforms (selection into treatment).

* IV strategy: supply of French troops depended on distance between Paris and occupied territory.
<table><caption>Table 2. French effect on literacy rates in 1861: OLS regressions</caption><thead><tr><th rowspan="2"></th><th colspan="4">Literacy rate in 1861 (population age &gt; 4)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>French political control</td><td>0.090***<br>(0.027)</td><td>0.045***<br>(0.014)</td><td>0.052***<br>(0.012)</td><td>0.074***<br>(0.015)</td></tr><tr><td>Density of Jesuit collegia before French</td><td>3.661***<br>(1.020)</td><td>3.693***<br>(1.069)</td><td>0.200<br>(0.894)</td><td>0.674<br>(0.963)</td></tr><tr><td>Kingdom of Sardinia</td><td>11.926***<br>(4.058)</td><td>6.604**<br>(2.652)</td><td>8.153***<br>(1.870)</td><td>9.800***<br>(2.161)</td></tr><tr><td>Lombardo Veneto</td><td>19.266***<br>(3.133)</td><td>15.340***<br>(4.431)</td><td>15.869***<br>(3.558)</td><td>14.256***<br>(3.571)</td></tr><tr><td>Papal State</td><td>7.690***<br>(1.833)</td><td>6.671***<br>(1.942)</td><td>3.144**<br>(1.380)</td><td>1.166<br>(2.327)</td></tr><tr><td>Granduchy of Tuscany</td><td>4.673**<br>(3.390)</td><td>3.119<br>(2.850)</td><td>3.755**<br>(1.923)</td><td>4.935**<br>(2.128)</td></tr><tr><td>Kingdom of Two Sicilies</td><td>9.284***<br>(1.416)</td><td>9.302***<br>(1.672)</td><td>8.798***<br>(0.853)</td><td>3.221<br>(2.680)</td></tr><tr><td>Duchy of Parma and Piacenza</td><td>-6.869***<br>(1.725)</td><td>-8.594***<br>(2.459)</td><td>-7.527***<br>(1.503)</td><td>-7.089***<br>(1.529)</td></tr><tr><td>Duchy of Modena and Reggio</td><td>-6.890**<br>(2.806)</td><td>-6.203<br>(3.728)</td><td>-1.976<br>(3.365)</td><td>-2.444<br>(3.396)</td></tr><tr><td>Rainfalls</td><td>1.979**<br>(0.855)</td><td>3.264***<br>(0.647)</td><td>3.106***<br>(0.588)</td><td></td></tr><tr><td>Landlocked</td><td>2.258<br>(1.785)</td><td>1.575<br>(1.334)</td><td>1.450<br>(1.345)</td><td></td></tr><tr><td>Border</td><td>11.045***<br>(3.639)</td><td>12.292***<br>(3.155)</td><td>11.179***<br>(3.346)</td><td></td></tr><tr><td>Urbanization rate 1800</td><td></td><td>0.236***<br>(0.056)</td><td>0.192***<br>(0.063)</td><td></td></tr><tr><td>Export per capita</td><td></td><td></td><td>1.725**<br>(0.767)</td><td></td></tr><tr><td>Constant</td><td>11.653***<br>(3.224)</td><td>0.472<br>(6.965)</td><td>-12.488**<br>(4.952)</td><td>-21.511***<br>(5.879)</td></tr><tr><td>Observations</td><td>59</td><td>59</td><td>59</td><td>59</td></tr><tr><td>R-squared</td><td>0.834</td><td>0.894</td><td>0.934</td><td>0.937</td></tr><tr><td>Moran statistic</td><td>3.32</td><td>2.45</td><td>0.61</td><td>1.4</td></tr><tr><td>p-value</td><td>0.0686</td><td>0.1179</td><td>0.4366</td><td>0.2372</td></tr></tbody></table>

Robust standard errors in parentheses, **p < 0.01, **p < 0.05, *p < 0.1. Dummy for the Duchy of Lucca has been dropped because of collinearity.
<table><caption>Table 3. French effect on male and female literacy rates in 1861: OLS regressions</caption><thead><tr><th rowspan="2"></th><th colspan="4">Male literacy rate in 1861 (population age > 4)</th><th colspan="4">Female literacy rate in 1861 (population age > 4)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>French political control</td><td>0.105<sup>***</sup></td><td>0.046<sup>***</sup></td><td>0.053<sup>***</sup></td><td>0.078<sup>***</sup></td><td>0.075<sup>***</sup></td><td>0.043<sup>***</sup></td><td>0.050<sup>***</sup></td><td>0.069<sup>***</sup></td></tr><tr><td>Density of Jesuit collegia before French</td><td>(0.031)</td><td>(0.015)</td><td>(0.014)</td><td>(0.019)</td><td>(0.023)</td><td>(0.014)</td><td>(0.012)</td><td>(0.014)</td></tr><tr><td></td><td>(1.233)</td><td>(1.275)</td><td>(1.027)</td><td>(1.102)</td><td>(0.899)</td><td>(0.952)</td><td>(0.874)</td><td>(0.944)</td></tr><tr><td>Rainfalls</td><td></td><td>2.829<sup>***</sup></td><td>4.050<sup>***</sup></td><td>3.877<sup>***</sup></td><td></td><td>1.227</td><td>2.581<sup>***</sup></td><td>2.450<sup>***</sup></td></tr><tr><td></td><td></td><td>(0.943)</td><td>(0.774)</td><td>(0.725)</td><td></td><td>(0.824)</td><td>(0.596)</td><td>(0.554)</td></tr><tr><td>Landlocked</td><td></td><td>2.088</td><td>1.439</td><td>1.302</td><td></td><td>2.390</td><td>1.671</td><td>1.567</td></tr><tr><td></td><td></td><td>(1.984)</td><td>(1.614)</td><td>(1.632)</td><td></td><td>(1.751)</td><td>(1.268)</td><td>(1.281)</td></tr><tr><td>Border</td><td></td><td>13.390<sup>***</sup></td><td>14.575<sup>***</sup></td><td>13.358<sup>***</sup></td><td></td><td>8.990<sup>***</sup></td><td>10.304<sup>***</sup></td><td>9.380<sup>***</sup></td></tr><tr><td></td><td></td><td>(3.694)</td><td>(3.485)</td><td>(3.669)</td><td></td><td>(3.956)</td><td>(3.250)</td><td>(3.456)</td></tr><tr><td>Urbanization rate 1800</td><td></td><td></td><td>0.225<sup>***</sup></td><td>0.176<sup>**</sup></td><td></td><td></td><td>0.249<sup>***</sup></td><td>0.212<sup>***</sup></td></tr><tr><td>Export per capita</td><td></td><td></td><td>(0.064)</td><td>(0.076)</td><td></td><td></td><td>(0.050)</td><td>(0.057)</td></tr><tr><td></td><td></td><td></td><td></td><td>1.885<sup>*</sup></td><td></td><td></td><td></td><td>1.432<sup>**</sup></td></tr><tr><td>Dummies pre-unitary kingdoms</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Constant</td><td>19.847<sup>***</sup></td><td>3.244</td><td>-9.062</td><td>-18.920<sup>**</sup></td><td>4.147</td><td>-2.248</td><td>-15.897<sup>***</sup></td><td>-23.386<sup>***</sup></td></tr><tr><td></td><td>(3.772)</td><td>(7.801)</td><td>(6.072)</td><td>(7.536)</td><td>(2.747)</td><td>(6.509)</td><td>(4.382)</td><td>(4.952)</td></tr><tr><td>Observations</td><td>59</td><td>59</td><td>59</td><td>59</td><td>59</td><td>59</td><td>59</td><td>59</td></tr><tr><td>R-squared</td><td>0.811</td><td>0.892</td><td>0.924</td><td>0.928</td><td>0.840</td><td>0.882</td><td>0.930</td><td>0.932</td></tr><tr><td>Moran statistic</td><td>3.23</td><td>1.53</td><td>0.62</td><td>1.03</td><td>1.96</td><td>1.51</td><td>1.28</td><td>1.43</td></tr><tr><td>p-value</td><td>0.0723</td><td>0.2166</td><td>0.4303</td><td>0.3113</td><td>0.1614</td><td>0.2195</td><td>0.2571</td><td>0.1189</td></tr></tbody></table>

Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
<figure><img src="image_135.png" /></figure>

Source: Our computation on Ministero della Guerra (1808). See appendix A6

<caption>Figure 4. Provincial distance to Paris in hundreds of kilometres.</caption>
# Main Results

<table><caption>Table 4. Instrument relevance and validity</caption><thead><tr><th rowspan="2">Dependent variables</th><th colspan="2">Panel A</th><th colspan="2">Panel B</th></tr><tr><th>French political control</th><th></th><th>Density of Jesuit <i>collegia</i> before French</th><th>Urbanization rate before French</th></tr></thead><tbody><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td>Distance to Paris</td><td>-7.292***<br>(0.755)</td><td>-8.179***<br>(2.449)</td><td>0.007<br>(0.039)</td><td>0.702<br>(1.176)</td></tr><tr><td>Density of Jesuit <i>collegia</i> before French</td><td></td><td>-7.263<br>(4.887)</td><td></td><td></td></tr><tr><td>Rainfalls</td><td></td><td>9.643**<br>(4.677)</td><td>-0.008<br>(0.103)</td><td>-5.314<br>(3.223)</td></tr><tr><td>Landlocked</td><td></td><td>-5.646<br>(9.920)</td><td>-0.353<br>(0.301)</td><td>-1.968<br>(6.970)</td></tr><tr><td>Border</td><td></td><td>17.942**<br>(8.852)</td><td>0.178<br>(0.125)</td><td>-2.545<br>(6.526)</td></tr><tr><td>Urbanization rate before French</td><td></td><td>0.449*<br>(0.248)</td><td></td><td></td></tr><tr><td>Export per capita</td><td></td><td>-17.938***<br>(6.648)</td><td></td><td></td></tr><tr><td>Dummies pre-unitary kingdoms</td><td>N</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Constant</td><td>209.415***<br>(10.033)</td><td>211.334***<br>(60.005)</td><td>-0.020<br>(1.055)</td><td>42.372<br>(35.287)</td></tr><tr><td>Observations</td><td>59</td><td>59</td><td>59</td><td>59</td></tr><tr><td>R-squared</td><td>0.561</td><td>0.854</td><td>0.166</td><td>0.240</td></tr></tbody></table>

Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1.
<table><caption>Table 5. French control and literacy rates of total population: 2SLS regressions</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th colspan="4">Literacy rate in 1861 (population age > 4)</th></tr></thead><tbody><tr><td>French political control</td><td>0.104<sup>***</sup><br>(0.030)</td><td>0.055<sup>**</sup><br>(0.021)</td><td>0.067<sup>***</sup><br>(0.020)</td><td>0.127<sup>***</sup><br>(0.038)</td></tr><tr><td>Density of Jesuit collegia before French</td><td>3.759<sup>***</sup><br>(1.108)</td><td>3.732<sup>***</sup><br>(1.091)</td><td>0.209<br>(0.937)</td><td>1.203<br>(1.172)</td></tr><tr><td>Rainfalls</td><td></td><td>1.676<sup>*</sup><br>(0.990)</td><td>2.815<sup>***</sup><br>(0.777)</td><td>2.127<sup>**</sup><br>(0.911)</td></tr><tr><td>Landlocked</td><td></td><td>2.302<br>(1.770)</td><td>1.632<br>(1.319)</td><td>1.417<br>(1.496)</td></tr><tr><td>Border</td><td></td><td>10.842<sup>***</sup><br>(3.579)</td><td>11.996<sup>***</sup><br>(3.084)</td><td>9.441<sup>***</sup><br>(3.275)</td></tr><tr><td>Urbanization rate 1800</td><td></td><td></td><td>0.240<sup>***</sup><br>(0.055)</td><td>0.151<sup>**</sup><br>(0.073)</td></tr><tr><td>Export per capita</td><td></td><td></td><td></td><td>3.593<sup>**</sup><br>(1.353)</td></tr><tr><td>Dummies pre-unitary kingdoms</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Constant</td><td>10.040<sup>***</sup><br>(3.669)</td><td>1.820<br>(7.262)</td><td>-10.596<sup>**</sup><br>(5.161)</td><td>-27.878<sup>***</sup><br>(6.685)</td></tr><tr><td>Observations</td><td>59</td><td>59</td><td>59</td><td>59</td></tr><tr><td>Distance to Paris</td><td>-13.260<sup>***</sup><br>(1.176)</td><td>-11.659<sup>***</sup><br>(1.572)</td><td>-11.661<sup>***</sup><br>(1.630)</td><td>-8.179<sup>***</sup><br>(2.449)</td></tr><tr><td>F-test (1st stage)</td><td>127.13</td><td>55.04</td><td>51.17</td><td>11.15</td></tr><tr><td>Moran statistic</td><td>4.56</td><td>2.86</td><td>0.41</td><td>0.83</td></tr><tr><td>p-value</td><td>0.033</td><td>0.091</td><td>0.524</td><td>0.362</td></tr></tbody></table>

Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1. A graphical representation of conditional correlations is shown in figure A1.
Long-term economic consequences of colonial institutions
within Western Europe?
* The AJR logic still holds, even in a Western context:

* Inclusive colonial institutions rather beneficial for long-run growth and human capital accumulation (Germany, Italy).

* Extractive colonial institutions rather detrimental for long-run growth and human capital accumulation (Spain).

* Timing is somewhat similar: the benefits generally occur upon industrialization.

* But unclear in German case, and no further analysis in Italian case.
Topic 3
State capacity, conflict, and development
What enables societies to adopt growth enhancing institutions?
* State capacity: concepts.

* State capacity and economic performance.

* Warfare and state capacity.

* Warfare and the Malthusian circle.

Dincecco and Katz (2016) Dincecco (2017) Johnson and Koyama (2017) Dincecco and Wang (2018) Dincecco, Fenske, and Onorato (2019)
* Ability of a state to accomplish its intended policy actions.

* Corresponds to infrastructural power of a state:

> "We might term this 'infrastructural power', the capacity of the state to penetrate civil society and to implement logistically political decisions throughout the realm." Mann (1986)
* Ability of a state to accomplish its intended policy actions.

* Corresponds to infrastructural power of a state:

> "We might term this 'infrastructural power', the capacity of the state to penetrate civil society and to implement logistically political decisions throughout the realm." Mann (1986)

* Ability of a state to collect taxes, enforce law and order, provide public goods.
* Two main elements:

* Legal capacity.

⇒ Ability to enforce rules across entire territory it claims to rule.
* Two main elements:

* Legal capacity.

⇒ Ability to enforce rules across entire territory it claims to rule.

* Fiscal capacity.

⇒ Ability to garner enough tax revenues to implement its policies.
* Two main elements:

* Legal capacity.
    ⇒ Ability to enforce rules across entire territory it claims to rule.

* Fiscal capacity.
    ⇒ Ability to garner enough tax revenues to implement its policies.

* State capacity requires degree of political and legal centralization.
* Paradox of power:

> "In framing a government which is to be administered by men over men, the great difficulty lies in this: you must first enable the government to control the governed; and in the next place oblige it to control itself." Madison (1788)
* Paradox of power:

> "In framing a government which is to be administered by men over men, the great difficulty lies in this: you must first enable the government to control the governed; and in the next place oblige it to control itself." Madison (1788)

* Effective statehood requires effective controls over the executive.
<figure><img src="image_136.png" /><figcaption>Log Tax Revenues per Capita</figcaption></figure>

Source: Johnson and Koyama (2017)

* State capacity increased in Europe.

* But considerable institutional variation.
* High fiscal capacity, constraints on executive, rule of law.
* High fiscal capacity, constraints on executive, rule of law.

* Nationwide and representative Parliament.

* Centralization of fiscal and legal institutions.
* High fiscal capacity, constraints on executive, rule of law.

* Nationwide and representative Parliament.

* Centralization of fiscal and legal institutions.

* One reason: history of homogeneous, coherent and centralized polity.
<figure><img src="image_137.png" /><figcaption>Per capita revenues, Britain</figcaption></figure>

Source: Dincecco (2009)
* Kingdom constituted over time through warfare and marriages:

* Dauphiné 1349, Burgundy 1477, Provence 1486, Brittany 1532 ...

* Collection of feudal appendages loosely held by loyalty to king.
* Kingdom constituted over time through warfare and marriages:

* Dauphiné 1349, Burgundy 1477, Provence 1486, Brittany 1532 ...

* Collection of feudal appendages loosely held by loyalty to king.

* France remained legally and fiscally fragmented until Revolution.

* Still, considerable improvements in state capacity 17th-18th centuries.
## Administrative Institutions in France (Subdélégations)

<figure><img src="image_138.png" /></figure>
## Judicial Institutions in France (Bailliages)

<figure><img src="image_139.png" /></figure>
## Judicial Institutions in France (Bailliages)

<figure><img src="image_140.png" /></figure>
<figure><img src="image_141.png" /><figcaption>Per capita revenues, France</figcaption></figure>

Source: Dincecco (2009)
* Heterogeneous initial conditions: high ethnolinguistic diversity.

* State capacity took longer to build.

* Coercive path to modern statehood e.g. life-long conscription.
<figure><img src="image_142.png" /><figcaption>## Per capita revenues, Prussia</figcaption></figure>

Source: Dincecco (2009)
<figure><img src="image_143.png" /><figcaption>Per capita revenues, Austria-Hungary</figcaption></figure>

Source: Dincecco (2009)
State capacity and economic performance in European history

<figure><img src="image_144.png" /></figure>

Source: Dincecco (2015)
* Systematic empirical analysis. Dincecco and Katz (2016)

* Two elements of state capacity:

* Fiscal centralization: year national government first secured revenues through uniform tax system.

* Limited government: year parliament gained stable constitutional right to control national budget annually.
State Capacity and Economic Growth

<table><thead><tr><td>Fiscal Centralization</td><td>Year</td><td>Limited Government</td><td>Year</td></tr></thead><tbody><tr><td>England</td><td>1066</td><td>England</td><td>1688</td></tr><tr><td>France</td><td>1790</td><td>France</td><td>1870</td></tr><tr><td>Belgium</td><td>1795</td><td>Belgium</td><td>1831</td></tr><tr><td>Piedmont</td><td>1802</td><td>Piedmont</td><td>1848</td></tr><tr><td>Netherlands</td><td>1806</td><td>Netherlands</td><td>1848</td></tr><tr><td>Prussia</td><td>1806</td><td>Prussia</td><td>1848</td></tr><tr><td>Spain</td><td>1845</td><td>Spain</td><td>1876</td></tr><tr><td>Austria</td><td>1848</td><td>Austria</td><td>1867</td></tr><tr><td>Portugal</td><td>1859</td><td>Portugal</td><td>1851</td></tr><tr><td>Sweden</td><td>1861</td><td>Sweden</td><td>1866</td></tr><tr><td>Denmark</td><td>1903</td><td>Denmark</td><td>1848</td></tr></tbody></table>
State Capacity and Economic Growth

<table><thead><tr><td>Fiscal Centralization</td><td>Year</td><td>Limited Government</td><td>Year</td></tr></thead><tbody><tr><td>England</td><td>1066</td><td>England</td><td>1688</td></tr><tr><td>France</td><td>1790</td><td>France</td><td>1870</td></tr><tr><td>Belgium</td><td>1795</td><td>Belgium</td><td>1831</td></tr><tr><td>Piedmont</td><td>1802</td><td>Piedmont</td><td>1848</td></tr><tr><td>Netherlands</td><td>1806</td><td>Netherlands</td><td>1848</td></tr><tr><td>Prussia</td><td>1806</td><td>Prussia</td><td>1848</td></tr><tr><td>Spain</td><td>1845</td><td>Spain</td><td>1876</td></tr><tr><td>Austria</td><td>1848</td><td>Austria</td><td>1867</td></tr><tr><td>Portugal</td><td>1859</td><td>Portugal</td><td>1851</td></tr><tr><td>Sweden</td><td>1861</td><td>Sweden</td><td>1866</td></tr><tr><td>Denmark</td><td>1903</td><td>Denmark</td><td>1848</td></tr></tbody></table>

* Measurement error: downward attenuation bias.
* Economic performance:

* Log annual growth rate of real GDP per capita, 1650–1913. Maddison

* Missing values linearly interpolated, especially 1650–1820.
* Economic performance:

* Log annual growth rate of real GDP per capita, 1650–1913. Maddison

* Missing values linearly interpolated, especially 1650–1820.

* State capacity: Dincecco (2011)

* Extractive capacity: government revenues per capita.

* Productive capacity: non-military expenditures per capita.

* Missing values linearly interpolated.
<figure><img src="image_145.png" /><figcaption>Time-demeaned per capita GDP growth rates and fiscal centralization</figcaption></figure>
Time-demeaned per capita GDP growth rates and limited government

<figure><img src="image_146.png" /></figure>
* Empirical strategy:

$$ \Delta y_{i,t} = \alpha_0 + \alpha_1 C_{i,t} + \alpha_2 L_{i,t} + X'_{i,t-1}\alpha_3 + \mu_i + \lambda_t + \varepsilon_{i,t} $$

* $\Delta y_{i,t}$: Log annual GDP per capita growth rate.

* $C_{i,t}$: indicator for fiscal centralization.

* $L_{i,t}$: indicator for limited government.

* $\mu_i$: country fixed effect.

* $\lambda_t$: year fixed effect.

* $X'_{i,t-1}$: conflict indicators, population growth, lagged y.
* Source of identification and identifying assumption:
* Source of identification and identifying assumption:

* Relative changes in growth rate across countries.

* Parallel counterfactual trends.
* Source of identification and identifying assumption:

* Relative changes in growth rate across countries.

* Parallel counterfactual trends.

* Identification issues:
* Source of identification and identifying assumption:

* Relative changes in growth rate across countries.

* Parallel counterfactual trends.

* Identification issues:

* Reverse causality: economic growth might promote political reforms, political changes might take place in times of downturns or upswing.

* Omitted variable bias: control for constant unobserved heterogeneity, but not time-varying.
* Source of identification and identifying assumption:

* Relative changes in growth rate across countries.

* Parallel counterfactual trends.

* Identification issues:

* Reverse causality: economic growth might promote political reforms, political changes might take place in times of downturns or upswing.

* Omitted variable bias: control for constant unobserved heterogeneity, but not time-varying.

* Not necessarily causal interpretation: document robust pattern.
State Capacity and Economic Growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Real GDP per capita growth</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Fiscal centralization</td><td>0.657***<br>[0.087]</td><td>0.222***<br>[0.086]</td><td>0.268***<br>[0.125]</td><td>0.344***<br>[0.171]</td></tr><tr><td>Limited government</td><td>0.321***<br>[0.109]</td><td>0.053<br>[0.165]</td><td>-0.028<br>[0.146]</td><td>-0.080<br>[0.205]</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country time trends</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Lags in y (2)</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>1,772</td><td>1,772</td><td>1,772</td><td>1,750</td></tr><tr><td>Countries</td><td>11</td><td>11</td><td>11</td><td>11</td></tr></tbody></table>
* Robustness:

* Include conflict and population growth controls.

* Drop after 1845.

* Averaged growth rates rather than annual.
* Relax assumption of constant impact over time:

$$ \Delta y_{i,t} = \alpha_0 + \sum_{j=1}^{5} \alpha_{1,j} \tilde{C}_{i,t}^{j} + \sum_{j=1}^{5} \alpha_{1,j} \tilde{L}_{i,t}^{j} + X'_{i,t-1} \alpha_3 + \mu_i + \lambda_t + \varepsilon_{i,t} $$

* $\tilde{C}_{i,t}^1, \tilde{L}_{i,t}^1 = 1$ for years 6-10 before.

* $\tilde{C}_{i,t}^2, \tilde{L}_{i,t}^2 = 1$ for years 1-5 before.

* $\tilde{C}_{i,t}^3, \tilde{L}_{i,t}^3 = 1$ for years 0-4 after.

* $\tilde{C}_{i,t}^4, \tilde{L}_{i,t}^4 = 1$ for years 5-10 after.

* $\tilde{C}_{i,t}^5, \tilde{L}_{i,t}^5 = 1$ for more than 10 years after.
Time-varying relationship between gdp per capita growth and fiscal centralization

<figure><img src="image_147.png" /></figure>
Time-varying relationship between gdp per capita growth and limited government

<figure><img src="image_148.png" /></figure>
* Important and direct role for fiscal centralization.

* Fiscally centralized regimes grew faster than fragmented:

* On average by 0.2–0.4% higher per year.

* Average 1650–1913: 0.6% ⇒ 1/4–2/3 of growth.
* Important and direct role for fiscal centralization.

* Fiscally centralized regimes grew faster than fragmented:

* On average by 0.2–0.4% higher per year.

* Average 1650–1913: 0.6% ⇒ 1/4–2/3 of growth.

* Long-lasting economic improvements from fiscal centralization.
* Test for relationship between political transformation and (extractive) state capacity:

$$ \Delta E_{i,t} = \alpha_0 + \alpha_1 C_{i,t} + \alpha_2 L_{i,t} + X'_{i,t-1}\alpha_3 + \mu_i + \lambda_t + \varepsilon_{i,t} $$

* $\Delta E_{i,t}$: Log annual growth rate in per capita revenues.
State Capacity and Economic Growth

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Revenues per capita growth</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Fiscal centralization</td><td>1.405***<br>[0.497]</td><td>1.467***<br>[0.531]</td><td>2.929***<br>[0.926]</td><td>3.923***<br>[0.907]</td></tr><tr><td>Limited government</td><td>0.438<br>[0.564]</td><td>0.047<br>[0.638]</td><td>0.717<br>[0.652]</td><td>1.601***<br>[0.807]</td></tr><tr><td>Country, year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country time trends</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Lags in y (2)</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>1,760</td><td>1,748</td><td>1,748</td><td>1,734</td></tr><tr><td>Countries</td><td>11</td><td>11</td><td>11</td><td>11</td></tr></tbody></table>
* Centralized regimes have 1.4–3.9% higher growth in extractive capacity.

* Some (less robust) evidence for correlation between limited government and productive capacity (non-military expenditure).
* Centralized regimes have 1.4–3.9% higher growth in extractive capacity.

* Some (less robust) evidence for correlation between limited government and productive capacity (non-military expenditure).

⇒ Political transformations larger impact on extractive capacity.
* Centralized regimes have 1.4–3.9% higher growth in extractive capacity.

* Some (less robust) evidence for correlation between limited government and productive capacity (non-military expenditure).

⇒ Political transformations larger impact on extractive capacity.

* Additional evidence on extractive capacity and GDP growth.
* Implement and enforce generalized institutions.
* Public goods provision:

* Defense (securing territorial borders).

* Transportation infrastructures (competitive market integration, spread of innovation).

* Mass education (investment in human capital).
* States and markets as complements.

* Competitive markets ensure allocative efficiency.

* Need public-order property rights and contracting institutions.

* Making fiscal revenues uniform and general increases market integration (no more internal tariffs).
* Nation-building.

* Early modern states were “stationary bandits”: taxes and war.

* ↑ state capacity ⇒ ↑ legitimacy.

* Ideology (e.g. nationalism), conscription, school helped build common national identities.

* Heterogeneity in preferences associated with less public goods, risk civil war, lower incomes.
* Warfare was instrumental for building state capacity.
* Warfare was instrumental for building state capacity.

* The rise of city-states in Western Europe.

* The rise of nation-states in Western Europe.

* The role of political geography: Europe, China, Africa.

* Warfare and political development.
* Aftermath of fall of Carolingian Empire (800s).
  High long-lasting political fragmentation.

* 500 small independent states in late medieval Europe. Tilly (1992)
* Aftermath of fall of Carolingian Empire (800s).
  High long-lasting political fragmentation.

* 500 small independent states in late medieval Europe. Tilly (1992)

* High political fragmentation + scarce territory ⇒ endemic warfare.

* 100+ major military conflict per century (1000–1789).
  Dincecco and Onorato (2017)
## Major military conflicts in Europe, 1000–1799

<figure><img src="image_149.png" /></figure>

Source: Dincecco and Wang (2018) based on Dincecco and Onorato (2017)
* Endemic warfare ⇌ institutional change in Europe.

“War made the state, and the state made war.” Tilly (1975)
* Endemic warfare ⇌ institutional change in Europe.
  "War made the state, and the state made war." Tilly (1975)

* Historical warfare very expensive.

* Securing revenues ⇌ rulers grant local privileges, e.g. representative political institutions, property rights protections.
  ⇌ formal role in governance of elite taxpayers.
* Endemic warfare ⇌ institutional change in Europe.
  "War made the state, and the state made war." Tilly (1975)

* Historical warfare very expensive.

* Securing revenues ⇌ rulers grant local privileges, e.g. representative political institutions, property rights protections.
  ⇌ formal role in governance of elite taxpayers.

* Strong empirical relationship between war frequency and parliamentary activity (1250–1800). Stasavage (2016)
* State capacity emerged earlier in city-states.

* Key issue: geographical scale for early nation-states:

* High local institutional fragmentation within (more later).

* Little control of executive: high monitoring costs. Stasavage (2011)
* State capacity emerged earlier in city-states.

* Key issue: geographical scale for early nation-states:

* High local institutional fragmentation within (more later).

* Little control of executive: high monitoring costs. Stasavage (2011)

* Visible in ability to borrow:

* City-states established long-term public debt long before territorial states.

* City-states borrow at lower cost than territorial states.
<figure><img src="image_150.png" /><figcaption>Interest rates on debts of city-states and territorial states</figcaption></figure>

Source: Stasavage (2011)
# Rise of City-States in Western Europe

<table><thead><tr><td>City State</td><td>Year</td><td>Territorial State</td><td>Year</td></tr></thead><tbody><tr><td>Arras</td><td>1241</td><td>Castile</td><td>1489</td></tr><tr><td>Venice</td><td>1262</td><td>Kingdom of Naples</td><td>1520</td></tr><tr><td>Siena</td><td>1290</td><td>France</td><td>1522</td></tr><tr><td>Bremen</td><td>1295</td><td>Holland</td><td>1522</td></tr><tr><td>Douai</td><td>1295</td><td>Papal States</td><td>1526</td></tr><tr><td>Hamburg</td><td>1308</td><td>Duchy of Milan</td><td>1543</td></tr><tr><td>Genoa</td><td>1340</td><td>Wurttemberg</td><td>1550</td></tr><tr><td>Florence</td><td>1347</td><td>Austria</td><td>1555</td></tr><tr><td>Barcelona</td><td>1360</td><td>Piedmont</td><td>1684</td></tr><tr><td>Cologne</td><td>1375</td><td>England</td><td>1693</td></tr><tr><td>Dortmund</td><td>1375</td><td>Tuscany</td><td>1700</td></tr><tr><td>Ghent</td><td>1375</td><td>Denmark</td><td>1725</td></tr><tr><td>Nuremberg</td><td>1381</td><td></td><td></td></tr><tr><td>Basel</td><td>1383</td><td></td><td></td></tr><tr><td>Zurich</td><td>1386</td><td></td><td></td></tr><tr><td>Mainz</td><td>1415</td><td></td><td></td></tr><tr><td>Bruges</td><td>1489</td><td></td><td></td></tr></tbody></table>
* Emergence in the early 16th century.

* However, remained weak for a very long time.

"The strength of a monarch's theoretical claims to absolutist rule was frequently inversely proportional to his de facto powers." Epstein (2000)
* Emergence in the early 16th century.

* However, remained weak for a very long time.

"The strength of a monarch's theoretical claims to absolutist rule was frequently inversely proportional to his de facto powers." Epstein (2000)

* Endemic warfare ⇒ relinquish partial political control to local elites for funds ⇒ city-states obstructed centralization efforts at national level.

* Early modern nation-States were mosaics built upon medley of local institutional structures.
* Classical argument. Dincecco (2017) Dincecco and Wang (2018)

* Low land-labor ratio ⇒ high marginal value of land ⇒ warfare.

* Warfare is expensive (military revolution) ⇒ need to raise taxes.

* Investment in fiscal capacity + political representation of local elites.
  ⇒ Political development ⇒ Economic development.
* Classical argument. Dincecco (2017) Dincecco and Wang (2018)

* Low land-labor ratio ⇒ high marginal value of land ⇒ warfare.

* Warfare is expensive (military revolution) ⇒ need to raise taxes.

* Investment in fiscal capacity + political representation of local elites.
  ⇒ Political development ⇒ Economic development.

* Is this logic universal?
<figure><img src="image_151.png" /><figcaption>Major military conflicts in China, 1000–1799</figcaption></figure>

Source: Dincecco and Wang (2018) based on Wang (2006)
* Violent warfare was main feature of Europe and China.
# Warfare in Europe and China

* Violent warfare was main feature of Europe and China.

* But the nature of warfare differed greatly:

* External (interstate) vs internal (intrastate) conflicts.

* Multidirectional vs unidirectional foreign attack threats.
# Warfare in Europe and China

* Violent warfare was main feature of Europe and China.

* But the nature of warfare differed greatly:

* External (interstate) vs internal (intrastate) conflicts.

* Multidirectional vs unidirectional foreign attack threats.

* China: 65% are rebellions and civil wars.

* China: 80% of external conflicts against Steppe nomads.
* Europe: political fragmentation.

* Aftermath of fall of Carolingian Empire (800s)

* 500 small independent states in late medieval Europe. Tilly (1992)

* 85 states on average 1000–1799.
* Europe: political fragmentation.

* Aftermath of fall of Carolingian Empire (800s)

* 500 small independent states in late medieval Europe. Tilly (1992)

* 85 states on average 1000–1799.

* China: political centralization.

* Qin (200 BCE): first unitary state in China.

* Early uniform written language, currency, measure and weights.

* 1.5 states on average 1000–1799.
# The Role of Political Geography: Model

* Political geography: a mediator between warfare and political development.
# The Role of Political Geography: Model

* Political geography: a mediator between warfare and political development.

* Ruler actions:

* To fund military defense, ruler taxes $t > 0$ on domestic elites.

* Tax through coercion or bargaining.
* Political geography: a mediator between warfare and political development.

* Ruler actions:

* To fund military defense, ruler taxes $t > 0$ on domestic elites.

* Tax through coercion or bargaining.

* Elites actions:

* Move (exit): payoff $m \in \mathbb{R}$. Hirschman (1970) Clark et al. (2017)

* Contest: cost $c_E > 0 \implies$ cost to rule $c_R > 0$.

* Stay: ruler's payoff $l > 0$ for loyalty.

* If rule grands demands: payoff $p$ to elites, $-p$ to ruler.

* If ruler denies demands: payoff 0 to elites.
<figure><img src="image_152.png" /><figcaption>## Model and payoffs</figcaption></figure>
Context 1: Political Fragmentation

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

* Multidirectional attacks ⇌ high value of loyalty: $l \gg 0$.
Context 1: Political Fragmentation

• High political fragmentation ⇌ high potential for exit: $m > 0$.

• Multidirectional attacks ⇌ high value of loyalty: $l \gg 0$.

• Solve the game by backward induction:
Context 1: Political Fragmentation

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

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

* Solve the game by backward induction:

* $m > 0$ ⇌ elites move rather than stay in last stage.
# Context 1: Political Fragmentation

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

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

* Solve the game by backward induction:

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

* $l \gg 0 \Rightarrow$ ruler grants rather than deny ($l - p > 0$).
# Context 1: Political Fragmentation

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

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

* Solve the game by backward induction:

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

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

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

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

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

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

* Solve the game by backward induction:

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

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

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

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

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

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

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

* Solve the game by backward induction:

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

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

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

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

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

* Subgame perfect equilibrium: (Bargain, Grant; Contest, Stay, Move).
Context 2: Political Centralization

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

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

* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

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

* Solve the game by backward induction:
## Context 2: Political Centralization

* Low political fragmentation $\Rightarrow$ low potential for exit: $m < 0$.

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

* Solve the game by backward induction:

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

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

* Solve the game by backward induction:

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

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

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

* Solve the game by backward induction:

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

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

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

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

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

* Solve the game by backward induction:

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

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

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

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

* Ruler always coerces ($p > 0$).
Context 2: Political Centralization

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

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

• Solve the game by backward induction:

• $m < 0$ ⇐⇒ elites stay rather than move in last stage.

• $p > 0$ ⇐⇒ ruler denies rather than grant.

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

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

• Ruler always coerces ($p > 0$).

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

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

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

* Political representation helped build fiscal capacity.

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

* Political representation helped build fiscal capacity.

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

* In China:

* No representation so little agreement on taxes.

* Vast geography ⇒ communication costs and difficult monitoring.
<figure><img src="image_153.png" /><figcaption>Per capita revenue across Eurasia in the 1780s</figcaption></figure>
* Does this logic apply in Sub-Saharan Africa?
* Does this logic apply in Sub-Saharan Africa?

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

* Political geography.

* Transatlantic slave trade.

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

* Mostly raiding wars: much less expensive.

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

* Generated outgrowth of pre-colonial conflicts over people.

* Accentuated nature of intra-African wars.

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

* Transatlantic slave trade.

* Generated outgrowth of pre-colonial conflicts over people.

* Accentuated nature of intra-African wars.

* Slaves more valuable than taxes ⇌ ↓ fiscal capacity.

* European colonization.

* Scramble for Africa created conditions for persistence of conflict.

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

* Transatlantic slave trade.

* Generated outgrowth of pre-colonial conflicts over people.

* Accentuated nature of intra-African wars.

* Slaves more valuable than taxes ⇌ ↓ fiscal capacity.

* European colonization.

* Scramble for Africa created conditions for persistence of conflict.

* Arbitrary borders ↑ fractionalization ⇌ persistent civil conflicts.

* Civil conflicts ⇌ capture of state by specific groups

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

* Historical conflict data.

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

* Assign conflicts to modern borders.

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

* Historical conflict data.

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

* Assign conflicts to modern borders.

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

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

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

* Historical conflict data.

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

* Assign conflicts to modern borders.

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

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

* Fiscal capacity (1990–2014).

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

* Income tax require large administrative capacity.

* Also: share of direct taxes, income taxes to GDP, tax revenues to GDP.
<figure><img src="image_154.png" /><figcaption>Historical conflict, 1400–1799</figcaption></figure>
$$y_i = \alpha + \beta \text{ Conflict}_i + \delta \text{ Conflict}_i \times \text{Africa} + x'_i\gamma + \mu_j + \varepsilon_i$$

* i indexes countries.

* $y_i$: measure of fiscal capacity today.

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

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

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

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

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

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

* Fiscal strength rather than government size.

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

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

* Alternative samples:

* Drop 18th century wars, Russia, China.

* Add New World.

* Intermediate outcomes:

* Infrastructural power of state (railroads in 1910).

* Share of years with conflict 1850–1899.
* Our hypothesis:

* Under political geography conditions, states make wars and wars make states.

* Political development ⇒ economic development.

* Explains Great divergence.
* Our hypothesis:

* Under political geography conditions, states make wars and wars make states.

* Political development ⇒ economic development.

* Explains Great divergence.

* Alternative (demographic) hypothesis: Voigtlander and Voth (2013)

* In a Malthusian world.

* Wars ⇒ demography ⇒ economic development.
* Driven by two characteristics: war and death.
* Driven by two characteristics: war and death.

* Malthusian world:

* Amount of land per capita $\Rightarrow$ output per capita.
* Driven by two characteristics: war and death.

* Malthusian world:

* Amount of land per capita $\Rightarrow$ output per capita.

* $\uparrow$ death $\Rightarrow$ $\uparrow$ land-labor ratio in agriculture $\Rightarrow$ $\uparrow$ income per capita.
* Driven by two characteristics: war and death.

* Malthusian world:

* Amount of land per capita $\Rightarrow$ output per capita.

* $\uparrow$ death $\Rightarrow$ $\uparrow$ land-labor ratio in agriculture $\Rightarrow$ $\uparrow$ income per capita.

* Feedback loop: $\uparrow$ income per capita $\Rightarrow$ $\uparrow$ wars $\Rightarrow$ $\uparrow$ death...
# Puzzle: Sustained Riches in a Malthusian World

* Malthusian reasoning:
# Puzzle: Sustained Riches in a Malthusian World

* Malthusian reasoning:

* ↑ income ⇒ ↑ population growth. Malthus (1798)
# Puzzle: Sustained Riches in a Malthusian World

* Malthusian reasoning:

* ↑ income ⇒ ↑ population growth. Malthus (1798)

* Land is main input factor and fixed ⇒ ↓ marginal returns to labor.
# Puzzle: Sustained Riches in a Malthusian World

* Malthusian reasoning:

* ↑ income ⇒ ↑ population growth. Malthus (1798)

* Land is main input factor and fixed ⇒ ↓ marginal returns to labor.

* ↑ Population growth ⇒ ↓ wage rates.
# Puzzle: Sustained Riches in a Malthusian World

* Malthusian reasoning:

* ↑ income ⇒ ↑ population growth. Malthus (1798)

* Land is main input factor and fixed ⇒ ↓ marginal returns to labor.

* ↑ Population growth ⇒ ↓ wage rates.

* Industrial Revolution: escape Malthusian trap through fast technological change.
# Puzzle: Sustained Riches in a Malthusian World

* Incomes in pre-1800 Europe peaked after Black Death of 1350.

* In a Malthusian world, should have returned to pre-plague levels.

* Technology or institutions cannot win against population:

* High rate of technological change pre-1800: 0.25–0.5 % annually.

* Moderate rate of population growth pre-1800: 3% annually.
# Puzzle: Sustained Riches in a Malthusian World

* Incomes in pre-1800 Europe peaked after Black Death of 1350.

* In a Malthusian world, should have returned to pre-plague levels.

* Technology or institutions cannot win against population:

* High rate of technological change pre-1800: 0.25-0.5 % annually.

* Moderate rate of population growth pre-1800: 3% annually.

* But higher incomes were sustained. Why?
* WWII Russia, Germany, Poland; WWI France: 15% population loss.
* WWII Russia, Germany, Poland; WWI France: 15% population loss.

* Religious wars in 16th-century France (36 years): 20%.

* Thirty Years' War in 17th-century Germany (30 years): 33%.

* Black Death in 14th-century Europe (4 years): 30–50%.
* Two determinants of deadliness of war:

* Power of weaponry (soldiers).

* Hunger and diseases (civilians).
* Two determinants of deadliness of war:

* Power of weaponry (soldiers).

* Hunger and diseases (civilians).

* Diseases: main driver of death pre-1800.

* Main channel: contact with alien armies (and trade).

* Aggravated by geographical and political fragmentation.
## Plague Outbreaks in Europe, 1350–1650

<figure><img src="image_155.png" /></figure>

Source: Biraben (1975).

Source: Voigtlander and Voth (2013)
## Frequency of War

<table><thead><tr><td>Century</td><td>Number of wars</td><td>Average duration (years)</td><td>Percentage of years under warfare</td></tr></thead><tbody><tr><td>16th</td><td>34</td><td>1.6</td><td>95%</td></tr><tr><td>17th</td><td>29</td><td>1.7</td><td>94%</td></tr><tr><td>18th</td><td>17</td><td>1.0</td><td>78%</td></tr><tr><td>19th</td><td>20</td><td>0.4</td><td>40%</td></tr><tr><td>20th</td><td>15</td><td>0.4</td><td>53%</td></tr></tbody></table>

Source: Tilly (1990).

Source: Voigtlander and Voth (2013)
* Given wars and death, why higher income by 1700 than before and elsewhere?
* Given wars and death, why higher income by 1700 than before and elsewhere?

* Because most death from diseases, not violence.

⇒ little destruction of infrastructures and capital stock.

* Land, houses, livestock rebuilt quickly.

* Pre-1800 wars ⇒ ↑↑ land+capital to labor ratio ⇒ ↑↑ income.
1. Black Death ⇒ ↑ income ⇒ ↑ government revenues.

2. ↑ government revenues ⇒ ↑ wars ⇒ ↑ death.
## Tax Revenues in Europe

<table><thead><tr><th>Year</th><th>Total tax revenue<br>(tons of silver)</th><th>Average tax per capita<br>(daily urban wage equivalents)</th></tr></thead><tbody><tr><td>1509</td><td>214</td><td>3.7</td></tr><tr><td>1559</td><td>456</td><td>3.6</td></tr><tr><td>1609</td><td>1,116</td><td>4.9</td></tr><tr><td>1659</td><td>2,215</td><td>5.7</td></tr><tr><td>1709</td><td>2,667</td><td>8.1</td></tr><tr><td>1759</td><td>3,808</td><td>9.9</td></tr><tr><td>1789</td><td>6,846</td><td>12.2</td></tr></tbody></table>

* Driven by ↑ income (taxes = 1/3).

* 70–80% spent on wars.

* Military revolution: wars became very expensive.
# A Malthusian Circle: Wars and Rising Riches

<figure><img src="image_156.png" /><figcaption>## Wars and Urbanization, 1300–1700</figcaption></figure>
# A Malthusian Circle: Wars and Rising Riches

<figure><img src="image_157.png" /><figcaption>## Wars and per capita GDP, 1500–1700</figcaption></figure>
* Their argument:

* Wars explain the Little Divergence.

* Political change was not fast enough relative to demographic forces.
* Their argument:

* Wars explain the Little Divergence.

* Political change was not fast enough relative to demographic forces.

* What is missing in their argument?
* Their argument:

* Wars explain the Little Divergence.

* Political change was not fast enough relative to demographic forces.

* What is missing in their argument?

* Demographic forces might explain Little but not Great Divergence.
# A Malthusian Circle: Wars and Rising Riches

* Their argument:

* Wars explain the Little Divergence.

* Political change was not fast enough relative to demographic forces.

* What is missing in their argument?

* Demographic forces might explain Little but not Great Divergence.

* How to attain "↑ income per capita ⇒ ↑ wars"?
# A Malthusian Circle: Wars and Rising Riches

* Their argument:

* Wars explain the Little Divergence.

* Political change was not fast enough relative to demographic forces.

* What is missing in their argument?

* Demographic forces might explain Little but not Great Divergence.

* How to attain "↑ income per capita ⇒ ↑ wars"?

* Through ↑ fiscal capacity and political representation.
* Their argument:

* Wars explain the Little Divergence.

* Political change was not fast enough relative to demographic forces.

* What is missing in their argument?

* Demographic forces might explain Little but not Great Divergence.

* How to attain "↑ income per capita ⇒ ↑ wars"?

* Through ↑ fiscal capacity and political representation.

* Wars ⇒ State capacity ⇒ development at critical junctures.

* Malthusian circle: additional element for war-state link.
How path-dependent is the relationship between State capacity and long run development?
<figure><img src="image_158.png" /><figcaption>GDP per Capita in Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>
State capacity and economic performance in European history

<figure><img src="image_159.png" /></figure>
* Our explanation: wars and state, conditioned on the nature of warfare and political geography.

* How persistent is this relationship over time? Is it always linear?
* Our explanation: wars and state, conditioned on the nature of warfare and political geography.

* How persistent is this relationship over time? Is it always linear?

* Explore non-linearities in relationship between state persistence and growth, with a focus on China.
<figure><img src="image_160.png" /></figure>

# States and Markets: The Advantage of an Early Start*

VALERIE BOCKSTETTE

unaffiliated

AREENDAM CHANDA

Department of Economics, North Carolina State University, Raleigh, NC 27695

LOUIS PUTTERMAN

Department of Economics, Brown University, Providence, RI 02912
* Bockstette, Chanda, and Putterman (2002)

* What gives rise to effective states?

* Does state antiquity predict state capacity?

* Do countries with longer state history perform better?
* Periodization: 50-year intervals from 0 to 1950 (39 periods).

* Three components (×50):

* Is there a government above the tribal level? +1

* Is this government foreign (colonizer) or locally based? +0.5 / +1

* How much of the territory was ruled by this government? +1 if > 50%, +0.75 if >25%. ...

* Compute for 119 countries.

* Combine with different discount rates. Baseline: $(1 + 0.05)^t$.

* Divide by the sample maximum (China) ⇒ index between 0 and 1.
<table><caption>Table 1. Regional averages of *statehist5* (weighted by 1960 population).</caption><thead><tr><th></th><th>Statehist5</th></tr></thead><tbody><tr><td>Europe</td><td>0.79</td></tr><tr><td>Asia</td><td>0.79</td></tr><tr><td>Middle East & North Africa</td><td>0.64</td></tr><tr><td>Sub-Saharan Africa</td><td>0.32</td></tr><tr><td>Latin America/Caribbean</td><td>0.30</td></tr><tr><td>North America</td><td>0.20</td></tr><tr><td>Oceania</td><td>0.16</td></tr><tr><td>Total</td><td>0.41</td></tr></tbody></table>
# Antiquity of the State

<table><caption>Table 2. Correlations with stateinst5.</caption><thead><tr><th>Political and Institutional Quality Indicators</th><th>Assassinations</th><th>Riots</th><th>Government Crises</th></tr></thead><tbody><tr><td>Correlation Sample size</td><td>-0.1733‡<br>96</td><td>0.1869‡<br>92</td><td>0.2627*<br>99</td></tr><tr><td></td><td>Political stability</td><td>Lack of corruption</td><td>Lack of Government repudiation of contracts</td></tr><tr><td>Correlation Sample size</td><td>0.2437‡<br>62</td><td>0.3800*<br>90</td><td>0.5005*<br>90</td></tr><tr><td></td><td>Lack of expropriative risk</td><td>Rule of law</td><td>Bureaucratic quality</td></tr><tr><td>Correlation Sample size</td><td>0.4559*<br>90</td><td>0.3995*<br>90</td><td>0.3911*<br>90</td></tr><tr><td>Social and Demographic Indicators</td><td>Ethno-linguistic fragmentation</td><td>Social development‡</td><td>Population density 1960</td></tr><tr><td>Correlation Sample size</td><td>-0.2985*<br>98</td><td>0.4468*<br>39</td><td>0.1974*<br>103</td></tr><tr><td></td><td>Trust</td><td>Civic norms</td><td></td></tr><tr><td>Correlation Sample size</td><td>0.1227<br>29</td><td>0.3077*<br>29</td><td></td></tr><tr><td>GDP and Growth Indicators</td><td>GDP pc 1960</td><td>GDP pc 1970</td><td>GDP pc 1980</td></tr><tr><td>Correlation Sample size</td><td>0.2463<br>101</td><td>0.3380*<br>101</td><td>0.3746*<br>101</td></tr><tr><td></td><td>GDP pc 1990</td><td>GDP pc 1995</td><td>GDP growth 1960–1995</td></tr><tr><td>Correlation Sample size</td><td>0.4589*<br>101</td><td>0.4747*<br>101</td><td>0.5317*<br>94</td></tr></tbody></table>

Notes: *Statistically significant at the 0.01 level; †Statistically significant at the 0.05 level; ‡Statistically significant at the 0.10 level; §Excludes Latin America/Caribbean.
<figure><img src="image_161.png" /><figcaption>Figure 1. State history and economic growth.</figcaption></figure>
* Issue: OVB.

* No reverse causality.

* But state antiquity might proxy for more direct determinants (geographic region, population density, linguistic homogeneity...)

* Their solution: simple multivariate OLS.
<table><caption>Table 3. Regressions with *statehats*⁵ using growth (1960–1995) as the dependent variable.</caption><thead><tr><th></th><th>1</th><th>2</th><th>3<sup>†</sup></th><th>4<sup>†</sup></th><th>5<sup>†</sup></th><th>6<sup>†</sup></th><th>7<sup>†</sup></th></tr></thead><tbody><tr><td>Constant</td><td>0.049</td><td>0.029</td><td>0.036</td><td>0.021</td><td>0.029</td><td>0.035</td><td>0.029</td></tr><tr><td>Log of GDP pc (1960)</td><td>(2.692)*</td><td>(1.424)</td><td>(1.844)***</td><td>(1.06)</td><td>(1.51)</td><td>(1.6)</td><td>(1.04)</td></tr><tr><td></td><td>-0.011</td><td>-0.009</td><td>-0.009</td><td>-0.009</td><td>-0.008</td><td>-0.009</td><td>-0.009</td></tr><tr><td></td><td>(-4.237)*</td><td>(-3.119)*</td><td>(-3.515)*</td><td>(-3.01)*</td><td>(-3.21)*</td><td>(-3.21)*</td><td>(-2.62)**</td></tr><tr><td>Schooling</td><td>0.033</td><td>0.032</td><td>0.034</td><td>0.026</td><td>0.029</td><td>0.032</td><td>0.004</td></tr><tr><td></td><td>(2.559)**</td><td>(2.654)*</td><td>(2.879)*</td><td>(2.33)**</td><td>(2.62)*</td><td>(2.71)*</td><td>(0.36)</td></tr><tr><td>Log of population growth (1960–1995)</td><td>-0.001</td><td>0.002</td><td>0.001</td><td>0.007</td><td>0.002</td><td>0.002</td><td>0.002</td></tr><tr><td></td><td>(0.477)</td><td>(0.797)</td><td>(0.602)</td><td>(2.47)**</td><td>(0.86)</td><td>(0.85)</td><td>(0.72)</td></tr><tr><td>Log of investment rate (1960–1995)</td><td>0.017</td><td>0.014</td><td>0.014</td><td>0.012</td><td>0.013</td><td>0.014</td><td>0.009</td></tr><tr><td></td><td>(5.854)*</td><td>(5.468)*</td><td>(5.396)*</td><td>(5.04)*</td><td>(5.51)*</td><td>(5.16)*</td><td>(2.64)*</td></tr><tr><td>Statehats<sup>5</sup></td><td></td><td>0.025</td><td>0.021</td><td>0.029</td><td>0.021</td><td>0.025</td><td>0.028</td></tr><tr><td></td><td></td><td>(3.586)*</td><td>(3.372)*</td><td>(4.45)*</td><td>(3.49)*</td><td>(3.63)*</td><td>(3.41)*</td></tr><tr><td>ICRG (Institutional Quality)</td><td></td><td></td><td></td><td>0.002</td><td></td><td></td><td>0.001</td></tr><tr><td></td><td></td><td></td><td></td><td>(2.29)**</td><td></td><td></td><td>(1.39)</td></tr><tr><td>Population density (1960)</td><td></td><td></td><td></td><td></td><td>0.001</td><td></td><td>0.001</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>(5.5)*</td><td></td><td>(6.0)*</td></tr><tr><td>ETHNIC</td><td></td><td></td><td></td><td></td><td></td><td>-0.004</td><td>-0.002</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>(-0.84)</td><td>(-0.50)</td></tr><tr><td>East-Asia Pacific</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.014</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(2.82)*</td></tr><tr><td>Latin America</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.006</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(1.50)</td></tr><tr><td>Middle East and North Africa</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.01</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(2.72)*</td></tr><tr><td>North America</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.018</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(2.63)**</td></tr><tr><td>South Asia</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.008</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(0.16)</td></tr><tr><td>Sub Saharan Africa</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.0002</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(-0.03)</td></tr><tr><td>Western Europe</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.006</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(1.01)</td></tr><tr><td>Observations</td><td>88</td><td>88</td><td>87</td><td>77</td><td>86</td><td>82</td><td>73</td></tr><tr><td>R-square</td><td>0.47</td><td>0.58</td><td>0.58</td><td>0.65</td><td>0.65</td><td>0.6</td><td>0.77</td></tr></tbody></table>

Notes: [†] Hong Kong has been dropped from regressions (3)–(7). Numbers in parentheses are t statistics (calculated from heteroscedastic consistent standard errors). Schooling refers to secondary school enrollment ratio in 1960. Institutional quality is measured by the ICRG index. ETHNIC is the variable used in Easterly and Levinson (1997). * = significant at 0.01 level; ** = significant at 0.05 level; *** = significant at 0.10 level.
<figure><img src="image_162.png" /><figcaption>Figure 2. State history and economic growth.</figcaption></figure>
<table><caption>Table 4. Regressions with *statehist5* using growth (1960–1995) as the dependent variable (non OECD countries only).</caption><thead><tr><th></th><th>1</th><th>2</th><th>3†</th><th>4†</th><th>5†</th><th>6†</th><th>7†</th></tr></thead><tbody><tr><td>Constant</td><td>0.043<br>(2.16)**</td><td>0.025<br>(1.05)</td><td>0.032<br>(1.46)</td><td>0.0002<br>(0.01)</td><td>0.027<br>(1.23)</td><td>0.03<br>(1.18)</td><td>0.022<br>(0.91)</td></tr><tr><td>Log of GDP<br>per $60</td><td>-0.01<br>(-3.66)*</td><td>-0.008<br>(-2.37)**</td><td>-0.009<br>(-2.81)*</td><td>-0.006<br>(-2.32)**</td><td>-0.008<br>(-2.57)**</td><td>-0.008<br>(-2.43)**</td><td>-0.007<br>(-1.78)**</td></tr><tr><td>Schooling</td><td>0.066<br>(3.14)*</td><td>0.052<br>(2.29)**</td><td>0.056<br>(2.52)**</td><td>0.045<br>(2.63)**</td><td>0.044<br>(2.00)**</td><td>0.049<br>(2.02)**</td><td>0.021<br>(1.11)</td></tr><tr><td>Log of population<br>growth (1960–1995)</td><td>0.002<br>(0.66)</td><td>0.0002<br>(0.005)</td><td>0.0006<br>(0.136)</td><td>0.004<br>(1.14)</td><td>0.001<br>(0.27)</td><td>-0.0008<br>(-0.18)</td><td>0.003<br>(0.67)</td></tr><tr><td>Log of investment rate<br>(1960–1995)</td><td>0.013<br>(4.48)*</td><td>0.012<br>(4.37)*</td><td>0.012<br>(4.34)*</td><td>0.008<br>(3.13)*</td><td>0.012<br>(4.37)*</td><td>0.012<br>(4.15)*</td><td>0.007<br>(1.83)**</td></tr><tr><td><i>Statehist5</i></td><td>0.03<br>(3.29)*</td><td>0.025<br>(2.96)*</td><td>0.037<br>(4.42)*</td><td>0.024<br>(2.96)*</td><td>0.035<br>(3.72)*</td><td>0.048<br>(4.70)*</td><td></td></tr><tr><td>ICRG<br>(Institutional quality)</td><td></td><td></td><td></td><td>0.004<br>(3.71)*</td><td></td><td></td><td>0.003<br>(2.16)**</td></tr><tr><td>Population density<br>(1960)</td><td></td><td></td><td></td><td></td><td>0.001<br>(5.88)*</td><td></td><td>0.007<br>(4.20)*</td></tr><tr><td>ETHNIC</td><td></td><td></td><td></td><td></td><td></td><td>-0.003<br>(-0.68)</td><td>-0.003<br>(-0.38)</td></tr><tr><td>East-Asia Pacific</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.005<br>(-0.73)</td></tr><tr><td>Latin America</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.01<br>(-1.42)</td></tr><tr><td>Middle East and<br>North Africa</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.012<br>(-1.96)**</td></tr><tr><td>South Asia</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.022<br>(-3.36)*</td></tr><tr><td>Sub Saharan<br>Africa</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.014<br>(-1.50)</td></tr><tr><td>Observations</td><td>70</td><td>70</td><td>69</td><td>59</td><td>68</td><td>64</td><td>55</td></tr><tr><td>R-square</td><td>0.46</td><td>0.59</td><td>0.57</td><td>0.72</td><td>0.63</td><td>0.60</td><td>0.80</td></tr></tbody></table>

Notes: †Hong Kong has been dropped from regressions (3)–(7). Numbers in parentheses are *t* statistics (calculated from heteroscedastic consistent standard errors). Schooling refers to secondary school enrollment ratio in 1960. Institutional quality is as measured by the ICRG index. ETHNIC is the variable used in Europe (Levine 1997). Note that there are no West European countries that are not in the ICRG index and hence there is no dummy variable for this group. * = significant at 0.01 level. ** = significant at 0.05 level; *** = significant at 0.10 level.
# Main Results

<table><caption>Table 5. Regressions with statehist5 using log of output per worker (1988) as dependent variable.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>Constant</td><td>8.897<br>(14.78)*</td><td>8.558<br>(15.53)*</td></tr><tr><td>Statehist5</td><td>0.742<br>(1.85)***</td><td>0.194<br>(0.50)</td></tr><tr><td>ETHNIC</td><td>-0.182<br>(-0.73)</td><td>-0.131<br>(-0.58)</td></tr><tr><td>Population Density<br>(1960)</td><td>0.041<br>(2.21)**</td><td>0.015<br>(0.84)</td></tr><tr><td>East-Asia Pacific</td><td>-0.06<br>(-0.11)</td><td>-0.493<br>(-0.93)</td></tr><tr><td>Latin America</td><td>-0.099<br>(-0.17)</td><td>-0.297<br>(-0.57)</td></tr><tr><td>Middle East &<br>North Africa</td><td>0.473<br>(0.86)</td><td>0.192<br>(0.38)</td></tr><tr><td>North America</td><td>1.957<br>(2.87)*</td><td>0.646<br>(0.94)</td></tr><tr><td>South Asia</td><td>-0.695<br>(-1.13)</td><td>-0.619<br>(-1.11)</td></tr><tr><td>Sub-Saharan Africa</td><td>-1.387<br>(-2.38)**</td><td>-1.453<br>(-2.75)*</td></tr><tr><td>Latitude</td><td>-0.011<br>(-3.02)*</td><td>-0.008<br>(-2.21)**</td></tr><tr><td>SI</td><td></td><td>1.642<br>(4.31)*</td></tr><tr><td>Observations</td><td>93</td><td>93</td></tr><tr><td>R-squared</td><td>0.79</td><td>0.81</td></tr></tbody></table>

Notes: Data for log output per worker and latitude are from Hall and Jones (1999). Numbers in parentheses are * statistics. ETHNIC is the variable used in Easterly and Levine (1997). SI refers to “social infrastructure” in Hall and Jones (1999). * = significant at 0.01 level; ** = significant at 0.05 level; *** = significant at 0.10 level.

Social infrastructure: combines index of government antidiversion policies and openness to international trade.
Table 6a. Hall-Jones social infrastructure equation with *statehist5* as an additional instrument.

Dependent variable: social infrastructure.

<table><thead><tr><th></th><th>(1)<br>All Countries</th><th>(2)<br>Non OECD Countries</th></tr></thead><tbody><tr><td>Constant</td><td>0.04<br>(0.33)</td><td>-0.16<br>(-1.4)</td></tr><tr><td>ENGFRAC</td><td>0.21<br>(2.13)<sup>†</sup></td><td>0.07<br>(0.58)</td></tr><tr><td>EURFRAC</td><td>0.14<br>(3.29)*</td><td>0.11<br>(2.51)<sup>†</sup></td></tr><tr><td>LOGFRANKROM</td><td>0.06<br>(1.64)</td><td>0.12<br>(3.80)*</td></tr><tr><td>LATITUDE</td><td>0.001<br>(0.58)</td><td>-0.004<br>(-3.04)*</td></tr><tr><td>Statehist5</td><td>0.49<br>(3.39)*</td><td>0.59<br>(3.92)*</td></tr><tr><td>Observations</td><td>101</td><td>77</td></tr><tr><td>R-square</td><td>0.41</td><td>0.28</td></tr></tbody></table>

Notes: All data except *statehist5* come from Hall and Jones (1999). Both regressions exclude Hong Kong.

* = significant at 0.01 level; † = significant at 0.05 level.
<figure><img src="image_163.png" /></figure>

State history and economic development: evidence
from six millennia

Oana Borcan¹ · Ola Olsson²* · Louis Putterman³
* Borcan, Olsson, and Putterman (2018)

* Same question as Bockstette et al (2002) but extend to 3500 BCE through 2000 CE.

* Key theoretical prediction: modern productivity and population density have non-linear relationship with state experience in pre-industrial era.
* Key assumption: accumulated history ⇒ state capacity, taxation, public goods ⇒ economic growth.

* But remember state capacity: without constraints, extractive institutions and appropriation by elites ⇒ stagnation.

* Three predictions:

* In newly established states, increased capacity increases growth.

* In enduring states, increased capacity hurts growth.

* Spill-overs in state experience from surrounding high state antiquity through learning.
State History and Development

<figure><img src="image_164.png" /></figure>
<figure><img src="image_165.png" /></figure>
<figure><img src="image_166.png" /><figcaption>(c)</figcaption></figure>
* Index of state antiquity: extend Bockstette et al (2002) to 159 countries and 3500 BCE.

* Same method and periodization: 110 periods of 50 years.

* Is there a government above the tribal level? +1

* Is this government foreign (colonizer) or locally based? +0.5 / +1

* How much of the territory was ruled by this government? +1 if > 50%, +0.75 if >25%....

* Multiply all three then by 50.

* Then discount and normalize so that between 0 and 1.
<figure><img src="image_167.png" /></figure>
<figure><img src="image_168.png" /><figcaption>**Fig. 3** Emergence of states in four agricultural core areas and in the world as a whole 3500 BCE–2000 CE. **Note:** The figure shows the development of the aggregated State index in the Western agricultural zone (including 62 current countries in Europe, North Africa, the Middle East, as well as Afghanistan, Armenia, Azerbaijan, Georgia, India, Iran, Kazakhstan, Kyrgyzstan, Pakistan, Russia, Tajikistan, Turkmenistan, Uzbekistan), Eastern Asia (20 countries), Americas (including 27 countries in North and South America and in the Caribbean), and Sub-Saharan Africa (47 countries). Oceania (only 3 countries in our sample) is omitted. It also shows the aggregate index for the 159 countries in the world as a whole (solid black line). On the horizontal axis, negative values imply years BCE whereas positive values show the CE period. Particular years with trend breaks are marked</figcaption></figure>
$$ \text{Technology1500}_i = \beta_0 + \beta_1 \cdot \text{Statehist1500}_i + \beta_2 \cdot \text{Statehist1500}_i^2 + \beta'_j \cdot Z_i + \beta'_k \cdot X_i + \lambda_c + \epsilon_i \quad (5) $$

* First focus on relation in Malthusian era (1500).

* Simple cross-section OLS.

* Historical controls Z: time since transition to agriculture, first settlement of humans, state age in 1500.

* Geographic controls X: latitude, landlocked, distance to coast, elevation, land suitability for agriculture, temperature, rain.

* Continent fixed effects.

* When focusing on post-1500, use an ancestry-adjusted measure to account for population migrations.
<table><caption>Table 2 State history and average technology adoption in 1500 CE</caption><thead><tr><th></th><th colspan="7">Technology adoption in 1500 CE</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Statehist in 1500 CE</td><td>1.152***<br>(0.118)</td><td>2.643***<br>(0.328)</td><td></td><td>1.809***<br>(0.399)</td><td>1.535***<br>(0.306)</td><td>0.811***<br>(0.198)</td><td>1.168***<br>(0.275)</td></tr><tr><td>Statehist in 1500 CE squared</td><td></td><td>-2.993***<br>(0.645)</td><td></td><td>-2.585***<br>(0.744)</td><td>-1.501***<br>(0.543)</td><td>-0.340<br>(0.330)</td><td>-0.226<br>(0.306)</td></tr><tr><td>Ageyears in 1500 CE</td><td></td><td></td><td>0.104***<br>(0.008)</td><td>0.068***<br>(0.014)</td><td>0.038***<br>(0.012)</td><td>0.005<br>(0.010)</td><td>0.013<br>(0.011)</td></tr><tr><td>Origtime in 1500 CE</td><td></td><td></td><td></td><td></td><td>0.001<br>(0.001)</td><td>-0.001**<br>(0.001)</td><td>0.000<br>(0.001)</td></tr><tr><td>Origtime in 1500 CE squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.000)</td></tr><tr><td>State age in 1500 CE</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.075*<br>(0.043)</td></tr><tr><td>Observations</td><td>112</td><td>112</td><td>110</td><td>110</td><td>107</td><td>107</td><td>107</td></tr><tr><td>R-squared</td><td>0.446</td><td>0.558</td><td>0.532</td><td>0.641</td><td>0.818</td><td>0.904</td><td>0.912</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr></tbody></table>

The table displays OLS estimates from regressions of average technology adoption in 1500 CE on the extended *Statehist of 1500 CE, linear and squared*. The Average Technology Adoption index in 1500 CE is constructed by Comin et al. (2010). The list of controls includes: absolute latitude, an indicator of whether the present-day country is landlocked, distance to coast and rivers, mean elevation, land suitability, percentage arable land, temperature, precipitation, percentage population at risk of contracting malaria. Robust standard errors in parentheses

<footnote>***$p < 0.01$, **$p < 0.05$, *$p < 0.1$</footnote>