* Population:

* Ancient period (710–1192):

* 730: number and average size of administrative villages (assuming 1,250–400 inhabitants) and adding 10% of slaves and 150k urban dwellers. Sawada (1927), Kamata (1984), Farris (2009)

* 950: land area combined with land requirement per person, adding 15% of infants, 150k urban dwellers, 500k slaves. Alternative: number of administrative villages. Yokoyama (1879), Kito (1983), Farris (2009)

* 1150: same method as for 950.

* Medieval period (1192–1600):

* 1280: same method based on sample of land registers. Farris (2006)

* 1450: number of soldiers and applying a ratio for rural areas, and allowing for additional urban dwellers. Farris (2006)

* Tokugawa (1600–1868) and Meiji periods (1868–1912):

* 1600: projection from census of 1721. Saito and Takashima (2017)

* 1804, 1846, 1874: national surveys. Fukao et al. (2015)
Total population of Japan

<table><thead><tr><th colspan="5">A. Level in millions</th></tr><tr><th>Year</th><th>Average population</th><th>Minimum</th><th>Maximum</th><th>Alternative estimates</th></tr></thead><tbody><tr><td>730</td><td>6.1</td><td>5.8</td><td>6.4</td><td>4.5</td></tr><tr><td>950</td><td>5.0</td><td>4.4</td><td>5.6</td><td>6.4</td></tr><tr><td>1150</td><td>5.9</td><td>5.5</td><td>6.3</td><td>6.8</td></tr><tr><td>1280</td><td>6.0</td><td>5.7</td><td>6.2</td><td></td></tr><tr><td>1450</td><td>10.1</td><td>9.6</td><td>10.5</td><td></td></tr><tr><td>1600</td><td>17.0</td><td>16.2</td><td>17.8</td><td></td></tr><tr><td>1721</td><td>31.3</td><td>29.7</td><td>32.9</td><td></td></tr><tr><td>1804</td><td>30.7</td><td>29.2</td><td>32.2</td><td></td></tr><tr><td>1846</td><td>32.2</td><td>30.6</td><td>33.8</td><td></td></tr><tr><td>1874</td><td>34.5</td><td>32.8</td><td>36.2</td><td></td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
Annual growth rates (% per year)

<table><thead><tr><th colspan="3">B. Annual growth rates (% per year)</th></tr><tr><th>Years</th><th>Average population</th><th>Alternative estimates</th></tr></thead><tbody><tr><td>730-950</td><td>-0.09</td><td>0.16</td></tr><tr><td>950-1150</td><td>0.08</td><td>0.03</td></tr><tr><td>1150-1280</td><td>0.01</td><td>-0.10</td></tr><tr><td>1280-1450</td><td>0.31</td><td></td></tr><tr><td>1450-1600</td><td>0.35</td><td></td></tr><tr><td>1600-1721</td><td>0.51</td><td></td></tr><tr><td>1721-1804</td><td>-0.02</td><td></td></tr><tr><td>1804-1846</td><td>0.12</td><td></td></tr><tr><td>1846-1874</td><td>0.25</td><td></td></tr><tr><td>730-1280</td><td>0.00</td><td>0.05</td></tr><tr><td>1280-1721</td><td>0.38</td><td>0.38</td></tr><tr><td>1721-1874</td><td>0.06</td><td>0.06</td></tr><tr><td>730-1874</td><td>0.15</td><td>0.18</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
* Modest annual rate over period 730–1874: 0.15%.

* No Black Death or Mongol invasion.
* Modest annual rate over period 730-1874: 0.15%.

* No Black Death or Mongol invasion.

* 730-1280: stagnation (0%) due to frequent famines (1 every 3 years).

* 1280-1721: growth (0.38%) due to decline in frequency of famines (1 every 4 years, then 8 years after 1450).

* 1721-1846: decline (-0.02%) and slow growth (0.12%) with major famines in 1780s and 1830s.

* 1846-1874: recovery (0.25%), with proto-industrialization.
* Agriculture output from supply side.

* Ancient (710–1192) and medieval (1192–1600) periods: amount of arable land in use × estimates of productivity of land.

* Tokugawa (1600–1868) and early Meiji (1874) periods: direct estimation.

* High uncertainty, hence necessary to cross-validate with demand approach.
* Ancient period (under unified government):

* Labor restriction (*Ritsuryo*) system, where farmers cultivate allotted fields depending on family characteristics and paying land tax in rice and other poll taxes. Allotment of land revised every 6 years.

* Arable land from number of administrative villages (50 households) and paddy area per village. For non-paddy fields, area from temples and landholdings.

* Productivity of land for paddy fields based on legal and land-related documents. For non-paddy fields, use ratio of land values.
* Ancient period (under unified government):

* Labor restriction (*Ritsuryo*) system, where farmers cultivate allotted fields depending on family characteristics and paying land tax in rice and other poll taxes. Allotment of land revised every 6 years.

* Arable land from number of administrative villages (50 households) and paddy area per village. For non-paddy fields, area from temples and landholdings.

* Productivity of land for paddy fields based on legal and land-related documents. For non-paddy fields, use ratio of land values.

* Medieval period (no unified government): arable land and productivity from secondary literature based on (patchy) primary sources. Farris (2006) Nishitani (2015)
* Tokugawa (1600–1868) and early Meiji (1874) periods: direct estimation.

* Tokugawa: total agricultural output and land use from taxation system (kokudaka).

* Land area from census 1874 and cadastral survey for 1721.

* Procedures to make benchmark year measurements comparable.
Japanese agricultural production

<table><thead><tr><th colspan="6">A. Levels</th></tr><tr><th></th><th>Arable land<br>(1000 chō)</th><th>Land productivity<br>(koku/chō)</th><th>Agricultural output<br>(1000 koku)</th><th>Agricultural output<br>per head (koku)</th><th>Agricultural output<br>per head (1874 = 100)</th></tr></thead><tbody><tr><td>730</td><td>640</td><td>9.58</td><td>6130</td><td>1.00</td><td>54.0</td></tr><tr><td>950</td><td>1028</td><td>8.30</td><td>8527</td><td>1.71</td><td>91.6</td></tr><tr><td>1150</td><td>1109</td><td>8.31</td><td>9211</td><td>1.56</td><td>83.9</td></tr><tr><td>1280</td><td>1276</td><td>6.49</td><td>8278</td><td>1.39</td><td>74.7</td></tr><tr><td>1450</td><td>1621</td><td>8.60</td><td>13,938</td><td>1.39</td><td>74.5</td></tr><tr><td>1600</td><td>2497</td><td>10.36</td><td>25,879</td><td>1.52</td><td>81.8</td></tr><tr><td>1721</td><td>3249</td><td>12.67</td><td>41,173</td><td>1.32</td><td>70.7</td></tr><tr><td>1804</td><td>3892</td><td>12.74</td><td>49,604</td><td>1.62</td><td>86.8</td></tr><tr><td>1846</td><td>4265</td><td>13.26</td><td>56,571</td><td>1.76</td><td>94.3</td></tr><tr><td>1874</td><td>4533</td><td>14.31</td><td>64,861</td><td>1.86</td><td>100.0</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
Japanese agricultural production

B. Annual growth rates (% per year)

<table><thead><tr><th></th><th>Arable land</th><th>Land productivity</th><th>Agricultural output</th><th>Agricultural output per head</th></tr></thead><tbody><tr><td>730-950</td><td>0.22</td><td>-0.07</td><td>0.15</td><td>0.24</td></tr><tr><td>950-1150</td><td>0.04</td><td>0.00</td><td>0.04</td><td>-0.04</td></tr><tr><td>1150-1280</td><td>0.11</td><td>-0.19</td><td>-0.08</td><td>-0.09</td></tr><tr><td>1280-1450</td><td>0.14</td><td>0.17</td><td>0.31</td><td>0.00</td></tr><tr><td>1450-1600</td><td>0.29</td><td>0.12</td><td>0.41</td><td>0.06</td></tr><tr><td>1600-1721</td><td>0.22</td><td>0.17</td><td>0.38</td><td>-0.12</td></tr><tr><td>1721-1804</td><td>0.22</td><td>0.01</td><td>0.22</td><td>0.25</td></tr><tr><td>1804-1846</td><td>0.22</td><td>0.10</td><td>0.31</td><td>0.20</td></tr><tr><td>1846-1874</td><td>0.22</td><td>0.27</td><td>0.49</td><td>0.21</td></tr><tr><td>730-1280</td><td>0.13</td><td>-0.07</td><td>0.05</td><td>0.06</td></tr><tr><td>1280-1721</td><td>0.21</td><td>0.15</td><td>0.36</td><td>-0.01</td></tr><tr><td>1721-1874</td><td>0.22</td><td>0.08</td><td>0.30</td><td>0.23</td></tr><tr><td>730-1874</td><td>0.17</td><td>0.04</td><td>0.21</td><td>0.05</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
* Overall growth (730–1874): 0.21% per year with 4/5 from extension of arable area and 1/5 from rising land productivity.

* Most growth to keep up with population growth, esp. 730–1721 (Malthusian period).

* 1721–1874: escape Malthusian trap with per capita growth of 0.23%.
* Agriculture output from demand side.

* Indirect estimation of agricultural output through demand function approach. Allen (2000)

$$Q^A = rcN$$

* $Q^A$: real agricultural output.

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

* c: per capita consumption.

* N: population.
* Real consumption per capita:

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

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

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

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

* $y$: real income per capita.

* $\alpha_0$: constant.

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

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

* $\beta$: income elasticity of demand.
* Adapt this framework for Japan, where no prices other than for rice.

* Assume cross-price elasticity is 0 (instead of 0.1) $\implies \alpha_1 = -\beta$.
* Adapt this framework for Japan, where no prices other than for rice.

* Assume cross-price elasticity is 0 (instead of 0.1) $\implies \alpha_1 = -\beta$.

* No information on income, so use real wage as proxy.
* Adapt this framework for Japan, where no prices other than for rice.

* Assume cross-price elasticity is 0 (instead of 0.1) $\Rightarrow \alpha_1 = -\beta$.

* No information on income, so use real wage as proxy.

$$ \ln c = \alpha_0 + \alpha_1 \ln (P^A/P) + \beta \ln(W/P) $$

$$ \ln c = \alpha_0 - \beta \ln (P^A/P) + \beta \ln (W/P) $$

$$ \ln c = \alpha_0 + \beta \ln (W/P^A) $$

* $W/P^A$: "rice wages" i.e. daily wage / price of rice.
* Rice wage data 1260s–1840s from Bassino et al. (2010) and Bassino and Ma (2005)

* Constructed from nominal unskilled wages and price of rice, or from wages paid directly in rice.
* Rice wage data 1260s–1840s from Bassino et al. (2010) and Bassino and Ma (2005)

* Constructed from nominal unskilled wages and price of rice, or from wages paid directly in rice.

* Daily unskilled wages converted using 250 days worked per year.

* Converted in *koku*: one unit ≈ 150 kg of rice, enough to feed one person for one year (2,000 cal per day).
* Rice wage data 1260s–1840s from Bassino et al. (2010) and Bassino and Ma (2005)

* Constructed from nominal unskilled wages and price of rice, or from wages paid directly in rice.

* Daily unskilled wages converted using 250 days worked per year.

* Converted in *koku*: one unit ≈ 150 kg of rice, enough to feed one person for one year (2,000 cal per day).

* Convert into levels using supply side estimates of 1840s of 1.76 koku.
* Rice wage data 1260s–1840s from Bassino et al. (2010) and Bassino and Ma (2005)

* Constructed from nominal unskilled wages and price of rice, or from wages paid directly in rice.

* Daily unskilled wages converted using 250 days worked per year.

* Converted in *koku*: one unit ≈ 150 kg of rice, enough to feed one person for one year (2,000 cal per day).

* Convert into levels using supply side estimates of 1840s of 1.76 koku.

* Alternative sources of income (land rent): stable over time.
<figure><img src="image_162.png" /><figcaption>Japanese unskilled rice wage and agricultural demand per capita (log scale)</figcaption></figure>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
Per capita supply and demand for agricultural products (log scale)

<figure><img src="image_163.png" /></figure>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
* Supply-side: long time span but low frequency.

* Demand-side: short time span but high frequency.

* Similar increase in output from late-13th to mid-19th.

* Similar fluctuations.
* For industry and services: proxy with urbanization rate (share of population in urban centers).

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

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

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

* Use a 10,000 inhabitants threshold because of proto-industrialization.

* (Proto-industrialization implies increased productivity in non-agricultural sector outside cities, creating bias)
## Urban population and population density in Japan

<table><thead><tr><th></th><th>Urban population (1000)</th><th>Total population (millions)</th><th>Urban share (%)</th><th>Population density (persons per chō)</th></tr></thead><tbody><tr><td>730</td><td>124</td><td>6.1</td><td>2.0</td><td>0.21</td></tr><tr><td>950</td><td>135</td><td>5.0</td><td>2.7</td><td>0.17</td></tr><tr><td>1150</td><td>120</td><td>5.9</td><td>2.0</td><td>0.20</td></tr><tr><td>1280</td><td>208</td><td>6.0</td><td>3.5</td><td>0.21</td></tr><tr><td>1450</td><td>259</td><td>10.1</td><td>2.6</td><td>0.35</td></tr><tr><td>1600</td><td>1088</td><td>17.0</td><td>6.4</td><td>0.59</td></tr><tr><td>1721</td><td>3956</td><td>31.3</td><td>12.6</td><td>1.09</td></tr><tr><td>1804</td><td>3936</td><td>30.7</td><td>12.8</td><td>1.06</td></tr><tr><td>1846</td><td>3961</td><td>32.2</td><td>12.3</td><td>1.12</td></tr><tr><td>1874</td><td>3588</td><td>34.5</td><td>10.4</td><td>1.20</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
* Sharp increase in urbanization in Tokugawa period due to *Bakuhan* system separating peasants in country side and warriors in towns (and to some extent merchants and artisans).

* Account for proto-industrialization by including population density to affect non-agricultural output.

* Divide between secondary and tertiary based on relative weights derived in 1874, 1890, and 1909.
* Sharp increase in urbanization in Tokugawa period due to *Bakuhan* system separating peasants in country side and warriors in towns (and to some extent merchants and artisans).

* Account for proto-industrialization by including population density to affect non-agricultural output.

* Divide between secondary and tertiary based on relative weights derived in 1874, 1890, and 1909.

* Final adjustment: add 18.5% to agriculture to account for forestry and fishery based on benchmark in 1874.
B. Sectoral shares of GDP (%)

<table><thead><tr><th></th><th>Primary output</th><th>Secondary output</th><th>Tertiary output</th></tr></thead><tbody><tr><td>730</td><td>86.3</td><td>5.5</td><td>8.2</td></tr><tr><td>950</td><td>86.7</td><td>5.3</td><td>8.1</td></tr><tr><td>1150</td><td>86.5</td><td>5.5</td><td>8.1</td></tr><tr><td>1280</td><td>84.8</td><td>5.8</td><td>9.4</td></tr><tr><td>1450</td><td>82.2</td><td>6.8</td><td>11.0</td></tr><tr><td>1600</td><td>73.7</td><td>8.8</td><td>17.5</td></tr><tr><td>1721</td><td>62.9</td><td>10.9</td><td>26.2</td></tr><tr><td>1804</td><td>63.0</td><td>10.8</td><td>26.2</td></tr><tr><td>1846</td><td>62.7</td><td>10.9</td><td>26.3</td></tr><tr><td>1874</td><td>59.6</td><td>12.3</td><td>28.1</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
<table><thead><tr><th colspan="2">A. Level of GDP per capita</th><th rowspan="2">Population (million)</th><th rowspan="2">GDP per capita (koku)</th><th rowspan="2">GDP per capita (1874 = 100)</th></tr><tr><th></th><th>GDP (1000 koku)</th></tr></thead><tbody><tr><td>730</td><td>8422</td><td>6.1</td><td>1.38</td><td>37.2</td></tr><tr><td>950</td><td>11,664</td><td>5.0</td><td>2.33</td><td>62.8</td></tr><tr><td>1150</td><td>12,626</td><td>5.9</td><td>2.14</td><td>57.6</td></tr><tr><td>1280</td><td>11,571</td><td>6.0</td><td>1.94</td><td>52.4</td></tr><tr><td>1450</td><td>20,106</td><td>10.1</td><td>2.00</td><td>53.9</td></tr><tr><td>1600</td><td>41,635</td><td>17.0</td><td>2.45</td><td>66.0</td></tr><tr><td>1721</td><td>77,603</td><td>31.3</td><td>2.48</td><td>66.8</td></tr><tr><td>1804</td><td>93,296</td><td>30.7</td><td>3.04</td><td>81.9</td></tr><tr><td>1846</td><td>106,900</td><td>32.2</td><td>3.32</td><td>89.4</td></tr><tr><td>1874</td><td>128,176</td><td>34.8</td><td>3.71</td><td>100.0</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
## B. Annual growth rates of GDP per capita
Growth rate (% per year)

<table><tr><td>730-950</td><td>0.24</td></tr><tr><td>950-1150</td><td>-0.04</td></tr><tr><td>1150-1280</td><td>-0.07</td></tr><tr><td>1280-1450</td><td>0.02</td></tr><tr><td>1450-1600</td><td>0.13</td></tr><tr><td>1600-1721</td><td>0.01</td></tr><tr><td>1721-1804</td><td>0.25</td></tr><tr><td>1804-1846</td><td>0.21</td></tr><tr><td>1846-1874</td><td>0.40</td></tr><tr><td>730-1280</td><td>0.06</td></tr><tr><td>1280-1721</td><td>0.06</td></tr><tr><td>1721-1874</td><td>0.26</td></tr><tr><td>730-1874</td><td>0.09</td></tr></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
* Ancient period (710–1192): Malthusian economy, with rising living standards when population declines and vice-versa.

* Medieval period (1192–1600): stagnation then growth after 1450.

* Tokugawa period (1600–1868): stagnation then strong growth after 1721.

* Meiji period (1868–1912): take-off in early Meiji.

⇒ Transition to modern economic growth following Meiji builds upon the late Tokugawa period (post-1721).
* Medieval period (1192–1600):

* 1280–1450: stagnation, but high population growth with development of commerce, transportation, larger cities, along with institutional changes in manorial system.

* 1450–1600: acceleration with development of direct control of land by feudal lords, leading to improvements in agricultural productivity. Rapid growth in commercial activity overseas.
* Tokugawa period (1600–1868):

* 1600–1721: stagnation, but high population growth, driven by new castle towns and the reconfiguration of the Japanese geography.

* 1721–1868: strong growth with little population growth and proto-industrialization. Final period laying the foundations for modern economic growth achieved after Meiji restoration.
B. Reliability assessments for Japanese data

<table><thead><tr><th></th><th>Ancient</th><th>Medieval</th><th>Tokugawa</th></tr></thead><tbody><tr><td><strong>Primary output</strong></td><td></td><td></td><td></td></tr><tr><td>Arable land</td><td>C</td><td>C</td><td>A</td></tr><tr><td>Land productivity</td><td>D</td><td>C</td><td>B</td></tr><tr><td>Rice wage</td><td></td><td>C</td><td>B</td></tr><tr><td>Agricultural output</td><td>D</td><td>C</td><td>B</td></tr><tr><td><strong>Secondary and tertiary output</strong></td><td></td><td></td><td></td></tr><tr><td>Secondary output</td><td>C</td><td>C</td><td>B</td></tr><tr><td>Tertiary output</td><td>C</td><td>C</td><td>B</td></tr><tr><td><strong>Aggregates</strong></td><td></td><td></td><td></td></tr><tr><td>GDP</td><td>D</td><td>C</td><td>B</td></tr><tr><td>Population</td><td>C</td><td>B</td><td>A</td></tr><tr><td>GDP per capita</td><td>D</td><td>C</td><td>B</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
C. Sensitivity analysis for Japanese GDP

<table><thead><tr><th></th><th>Ancient</th><th>Medieval</th><th>Tokugawa</th></tr></thead><tbody><tr><th>Arable land</th><td>± 17.3%</td><td>± 16.7%</td><td>± 1.6%</td></tr><tr><th>Land productivity</th><td>± 35.2%</td><td>± 16.7%</td><td>± 6.5%</td></tr><tr><th>Secondary output</th><td>± 5.8%</td><td>± 1.3%</td><td>± 1.2%</td></tr><tr><th>Tertiary output</th><td>± 6.4%</td><td>± 2.0%</td><td>± 2.6%</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
<table><caption>Different scenarios for Japanese GDP per capita ($1990).</caption><thead><tr><th></th><th>Benchmark estimates</th><th>High estimates</th><th>Low estimates</th><th>Alternative estimates</th></tr></thead><tbody><tr><td>730</td><td>376</td><td>526</td><td>(300)</td><td>438</td></tr><tr><td>950</td><td>635</td><td>889</td><td>381</td><td>757</td></tr><tr><td>1150</td><td>583</td><td>816</td><td>350</td><td>680</td></tr><tr><td>1280</td><td>529</td><td>635</td><td>423</td><td>629</td></tr><tr><td>1450</td><td>545</td><td>654</td><td>436</td><td>614</td></tr><tr><td>1600</td><td>667</td><td>734</td><td>600</td><td>667</td></tr><tr><td>1721</td><td>675</td><td>743</td><td>608</td><td>675</td></tr><tr><td>1804</td><td>828</td><td>911</td><td>745</td><td>828</td></tr><tr><td>1846</td><td>903</td><td>993</td><td>813</td><td>903</td></tr><tr><td>1874</td><td>1,011</td><td>1,112</td><td>910</td><td>1,011</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
An Anglo-Japanese comparison of per capita GDP, 730–1874.

<table><thead><tr><th></th><th>Japan p.c. GDP, benchmark estimates ($1990)</th><th>GB p.c. GDP ($1990)</th><th>Japan/GB p.c. GDP, benchmark estimates (GB = 100)</th><th>Range for Japan/GB p.c. GDP (GB = 100)</th></tr></thead><tbody><tr><td>730</td><td>376</td><td></td><td></td><td></td></tr><tr><td>950</td><td>635</td><td></td><td></td><td></td></tr><tr><td>1150</td><td>583</td><td></td><td></td><td></td></tr><tr><td>1280</td><td>529</td><td>651</td><td>81.3</td><td>65.0–97.6</td></tr><tr><td>1450</td><td>545</td><td>1011</td><td>53.9</td><td>43.1–64.7</td></tr><tr><td>1600</td><td>667</td><td>1077</td><td>61.9</td><td>55.7–68.1</td></tr><tr><td>1721</td><td>675</td><td>1605</td><td>42.1</td><td>37.9–46.3</td></tr><tr><td>1804</td><td>828</td><td>2080</td><td>39.8</td><td>35.8–43.8</td></tr><tr><td>1846</td><td>903</td><td>2997</td><td>30.1</td><td>27.1–33.1</td></tr><tr><td>1874</td><td>1,011</td><td>4191</td><td>24.1</td><td>21.7–26.5</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
## GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)

<figure><img src="image_164.png" /></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015).
* Comparison Japan and Britain:

* Before Black Death (1280): Japan at 81% of Britain (65–98).

* No Black Death in Japan ⇌ decline to half of Britain in 1450.

* 1450–1600: gap narrows to 62%.

* 1600–1874: gap widens with British take-off to 24% despite Japanese take-off late 19th-century.

⇒ Despite long-run positive growth, divergence with Britain since 1280, and esp. after 1600.
GDP per capita levels in Europe and Asia

<table><thead><tr><th></th><th>GB</th><th>NL</th><th>Italy</th><th>Spain</th><th>Japan</th><th>China</th><th>India</th></tr></thead><tbody><tr><td>730</td><td></td><td></td><td></td><td></td><td>376</td><td></td><td></td></tr><tr><td>950</td><td></td><td></td><td></td><td></td><td>635</td><td></td><td></td></tr><tr><td>980</td><td></td><td></td><td></td><td></td><td></td><td>853</td><td></td></tr><tr><td>1020</td><td></td><td></td><td></td><td></td><td></td><td>1006</td><td></td></tr><tr><td>1050</td><td></td><td></td><td></td><td></td><td></td><td>967</td><td></td></tr><tr><td>1086</td><td>723</td><td></td><td></td><td></td><td></td><td>878</td><td></td></tr><tr><td>1120</td><td></td><td></td><td></td><td></td><td></td><td>863</td><td></td></tr><tr><td>1150</td><td></td><td></td><td></td><td></td><td>583</td><td></td><td></td></tr><tr><td>1280</td><td>651</td><td></td><td></td><td>897</td><td>529</td><td></td><td></td></tr><tr><td>1300</td><td>724</td><td></td><td>1466</td><td>889</td><td></td><td></td><td></td></tr><tr><td>1348</td><td>745</td><td>674</td><td>1327</td><td>957</td><td></td><td></td><td></td></tr><tr><td>1400</td><td>1045</td><td>958</td><td>1570</td><td>822</td><td></td><td>1032</td><td></td></tr><tr><td>1450</td><td>1011</td><td>1102</td><td>1657</td><td>827</td><td>545</td><td>990</td><td></td></tr><tr><td>1500</td><td>1068</td><td>1141</td><td>1408</td><td>826</td><td></td><td>858</td><td></td></tr><tr><td>1570</td><td>1096</td><td>1372</td><td>1325</td><td>919</td><td></td><td>885</td><td></td></tr><tr><td>1600</td><td>1077</td><td>1825</td><td>1224</td><td>876</td><td>667</td><td>865</td><td>682</td></tr><tr><td>1650</td><td>1055</td><td>1671</td><td>1372</td><td>838</td><td></td><td></td><td>638</td></tr><tr><td>1700</td><td>1563</td><td>1849</td><td>1344</td><td>817</td><td></td><td>1103</td><td>622</td></tr><tr><td>1720</td><td>1605</td><td>1751</td><td>1564</td><td>850</td><td>675</td><td>950</td><td></td></tr><tr><td>1750</td><td>1710</td><td>1877</td><td>1446</td><td>845</td><td></td><td>727</td><td>573</td></tr><tr><td>1800</td><td>2080</td><td>1974</td><td>1327</td><td>893</td><td>828</td><td>614</td><td>569</td></tr><tr><td>1850</td><td>2997</td><td>2397</td><td>1306</td><td>1144</td><td>903</td><td>600</td><td>556</td></tr><tr><td>1870</td><td>3856</td><td>2849</td><td>1470</td><td>1486</td><td>1011</td><td>618</td><td>526</td></tr></tbody></table>

Source: Bassino, Broadberry, Fukao, Gupta and Takashima (2019).
* Japan and the Little Divergence:

* Japan was relatively poor in Asia in around 1000.

* Remained poor and stagnant until growth in 1700s.

* No experience of a decline in 18th-century.

* Transition to modern growth in 19th-century.

⇒ Little Divergence with China circa 1700.
Ancient Economic History

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /></figure>

Toulouse
School of
Economics
How to study economic history when written records are scarce?
Matranga and Pascali (2021)

* Alternative sources of data besides written records:

* Paleoclimatic data

* Skeletons

* Structures

* Inscriptions
* Ice cores

* Pollen records

* Tree rings
* Ice cores

* Ice contains tiny air bubbles ⇒ chemistry of Earth's atmosphere when formed.

* Vostok Ice Core in Antarctica: data on temperature and CO2 concentration going back 400k years at 2k years intervals.

* But reflect average conditions across world (no regional variation).
* Paleopalynology: study of ancient pollen.

* Conifers and 12% of flowering plants are wind pollinated: preserved, e.g., at bottom of lakes.

* Date stratified samples through radiocarbon dating.

* Allows to reconstruct types of plants present, and therefore climate and cultivation in a location and time.

* Pollen stratigraphy enables to reconstruct panel data across regions.
Izbebski et al. (2020)

* Evidence of changes in structure of agricultural production and market integration in Ancient Greece based on pollen data.
* Periodization:

* 1000 BCE to 700 BCE: early Iron Age. Small-scale village systems engaged in subsistence farming.

* 700 BCE to 480 BCE: Archaic period. Expanding settlements and more expansive land-use systems in South; urbanization and autonomous city-states in East and North.

* 480 BCE to 323 BCE: Classical period. Growing urbanization with citizen farmers in South; Macedonian kingdom in North.

* 323 BCE to 31 BCE: Hellenistic period. Roman political and military influence.

* 31 BCE to 330 CE: imperial Roman period. Consolidation of imperial political and economic structures.
* Data of pollen records:

* European Pollen Database.

* Focus on sites from southern Greece.

* Include sites with at least one radiocarbon date for the last 3.5 millennia.

* 115 samples from 6 sites from 1219 BCE to 730 CE.

* Each sample documents the whole structure of vegetation around a given site at a particular point in time.

* Focus on pre-Roman market integration: Iron Age, Archaic, Classical, and Hellenistic periods (1000 BCE to 31 BCE).
## Pollen sites in Southern Greece

<figure><img src="image_168.png" /></figure>

Source: Izdebski et al. (2020)
Pollen sites in Southern Greece

<table><thead><tr><td>ID</td><td>Site name</td><td>No. of samples</td><td>Available dates</td></tr></thead><tbody><tr><td>1</td><td>Vravron</td><td>19</td><td>1069, 971, 872, 774, 676, 578, 479, 381, 283, 185, 86, 37 BCE, 61, 110, 209, 307, 405, 503, 730 CE</td></tr><tr><td>2</td><td>Elefsina</td><td>23</td><td>1078, 1040, 984, 946, 889, 852, 795, 672, 597, 484, 380, 276, 201, 87, 12 BCE, 101, 167, 262, 347, 460, 535, 564, 630 CE</td></tr><tr><td>3</td><td>Lerna</td><td>14</td><td>1045, 863, 688, 346, 265, 117 BCE, 15, 118, 209, 269, 374, 472, 555, 613 CE</td></tr><tr><td>4</td><td>Kotychi</td><td>11</td><td>1072, 525, 451, 378, 231, 157, 10 BCE, 63, 137, 210, 504 CE</td></tr><tr><td>5</td><td>Voulkaria</td><td>41</td><td>554, 535, 517, 498, 489, 472, 454, 437, 420, 402, 385, 368, 351, 334, 317, 299, 281, 263, 245, 226, 186, 166, 145, 124, 101, 78, 55, 30, 5 BCE, 21, 48, 76, 104, 165, 229, 262, 332, 406, 484, 565, 649 CE</td></tr><tr><td>6</td><td>Halos</td><td>7</td><td>1219, 833, 239 BCE, 155, 400, 542, 630 CE</td></tr></tbody></table>

Source: Izdebski et al. (2020)
* Aggregation of pollen data at regional level:

* Interpolation of missing eachs for each site and plant taxon using linear splines.

* Use panel data to predict prevalence of a given plant taxon at a time and place with flexible polynomial in time:

$$ \log (y_{it} + 1) = \sum_{k} \tau_{k} t^{k} + c_{i} + u_{it} $$

* $y_{it}$: raw percentage of pollen grains for given plant taxon at site i in year t.

* $t^k$: polynomial in time of degree k.

* $c_i$: site fixed effect.

* $u_{it}$: idiosyncratic error term.
Plant taxa (1/2)

Primary anthropogenic indicators

<table><tr><td>Cerealia-type</td><td> cereals</td></tr><tr><td>Olea</td><td> olive</td></tr><tr><td>Vitis</td><td> vine</td></tr><tr><td>Castanea</td><td> chestnut</td></tr><tr><td>Juglans</td><td> walnut</td></tr></table>

Secondary anthropogenic indicators

<table><tr><td>Cichorieae</td><td>a plant tribe incl. lettuce, chicory, dandelion, and salsify</td></tr><tr><td>Plantago lanceolata type</td><td> ribwort plantain</td></tr><tr><td>Rumex acetosa type</td><td> sorrel</td></tr><tr><td>Sanguisorba minor</td><td> salad burnet</td></tr></table>

Source: Izdebski et al. (2020)
Plant taxa (2/2)

<table><thead><tr><th colspan="2">Open vegetation</th></tr></thead><tbody><tr><td><i>Artenisia</i></td><td>a plant genus incl. mugwort, wormwood, and sagebrush</td></tr><tr><td><i>Chenopodiaceae</i></td><td>a plant family, also called the goosefoot family</td></tr><tr><td><i>Cyperaceae</i></td><td>sedges</td></tr><tr><td><i>Poaceae</i></td><td>grasses</td></tr></tbody><thead><tr><th colspan="2">Deciduous trees</th></tr></thead><tbody><tr><td><i>Alnus</i></td><td>alder</td></tr><tr><td><i>Carpinus betulus</i></td><td>common hornbeam</td></tr><tr><td><i>Corylus</i></td><td>hazel</td></tr><tr><td><i>Fagus</i></td><td>beech</td></tr><tr><td><i>Fraxinus ornus</i></td><td>manna ash</td></tr><tr><td><i>Quercus robur</i> type</td><td>deciduous oak</td></tr></tbody><thead><tr><th colspan="2">Evergreen trees</th></tr></thead><tbody><tr><td><i>Abies</i></td><td>fir</td></tr><tr><td><i>Juniperus</i></td><td>juniper</td></tr><tr><td><i>Pinus</i></td><td>pine</td></tr><tr><td><i>Quercus ilex</i> type</td><td>evergreen oak</td></tr></tbody></table>

Source: Izdebski et al. (2020)
Cereals in Southern Greece

<figure><img src="image_169.png" /></figure>

Source: Izdebski et al. (2020)
<figure><img src="image_170.png" /><figcaption>Olives in Southern Greece</figcaption></figure>

Source: Izdebski et al. (2020)
Vines in Southern Greece

<figure><img src="image_171.png" /></figure>

Source: Izdebski et al. (2020)
<figure><img src="image_172.png" /><figcaption>Cereals, olives, and vines in Southern Greece</figcaption></figure>

Source: Izdebski et al. (2020)
* If number of sites approximate demographic change:

* Demographic growth in southern Greece during early Iron Age, Archaic and Classical periods.

* Demographic decline in southern Greece during Hellenistic period.
* If number of sites approximate demographic change:

* Demographic growth in southern Greece during early Iron Age, Archaic and Classical periods.

* Demographic decline in southern Greece during Hellenistic period.

* Patterns of cultivation:

* Olive and vine increased in early Iron Age, Archaic, and Classical periods, but declined in Hellenistic period.

* Cereals decreased in early Iron Age and Archaic period, then increased in Classical and Hellenistic periods.
* Evidence of trade expansion before Roman empire:

* Despite demographic growth, cereal cultivation declines while olive and vine cultivation increase.

* Evidence of southern Greece developing an export economy based on cash cropping through olive and vine cultivation.

* Consistent with comparative advantage of this region.
* Validity of pollen-based trends.

* Number of sites from archaeological survey projects in Peloponnese (proxy for number of settlements): negatively correlated with pollen data from uncultivated plants.
* Validity of pollen-based trends.

* Number of sites from archaeological survey projects in Peloponnese (proxy for number of settlements): negatively correlated with pollen data from uncultivated plants.

* Mediterranean shipwrecks (proxy for maritime trade): positively correlated with trends in cash crops in Roman period. Before: light ships so less preservation.
* Validity of pollen-based trends.

* Number of sites from archaeological survey projects in Peloponnese (proxy for number of settlements): negatively correlated with pollen data from uncultivated plants.

* Mediterranean shipwrecks (proxy for maritime trade): positively correlated with trends in cash crops in Roman period. Before: light ships so less preservation.

* Large-scale oil and wine presses (proxy for capital investment): positively correlated with trends in cash crops in Roman period.
Demography in Southern Greece

<figure><img src="image_173.png" /></figure>

Source: Izdebski et al. (2020)
Demography and deciduous forest trees in Southern Greece

<figure><img src="image_174.png" /></figure>

Source: Izdebski et al. (2020)
Demography and grasses in Southern Greece

<figure><img src="image_175.png" /></figure>

Source: Izdebski et al. (2020)
## Pollen data versus shipwrecks in Southern Greece

<figure><img src="image_176.png" /></figure>

Source: Izdebski et al. (2020)
## Pollen data versus oil and wine presses in Southern Greece

<figure><img src="image_177.png" /></figure>

Source: Izdebski et al. (2020)
* Corroborating evidence:

* Historiography on the Archaic and Classical periods.
* Corroborating evidence:

* Historiography on the Archaic and Classical periods.

* Comparative advantage in olive cultivation from crop suitability data (ratio of total production capacity in t/ha of olive vs cereals).
<figure><img src="image_178.png" /><figcaption>Crop suitability in the Northeastern Mediterranean</figcaption></figure>

Source: Izdebski et al. (2020)
* Corroborating evidence:

* Differential trends in agricultural production associated with trade costs and distance to Black Sea colonies (major potential trade partner).

$$ \log (y_{ih} + 1) = \alpha_h + c_i + \delta \text{post}_h \cdot \log (d_i) + u_{ih} $$

* $y_{ih}$: percentage of pollen grains from given plant taxon at site i in half century h.

* $\alpha_h$: half century fixed effect.

* $c_i$: site fixed effect.

* post_h: indicator for Archaic and Classical periods.

* $d_i$: measure of trade cost between site i and Sigeion (ancient Greek city-state at Aegean entrance to Dardanelles).
* Corroborating evidence:

* Differential trends in agricultural production associated with trade costs and distance to Black Sea colonies (major potential trade partner).

$$\log (y_{ih} + 1) = \alpha_h + c_i + \delta \text{post}_h \cdot \log (d_i) + u_{ih}$$

* $y_{ih}$: percentage of pollen grains from given plant taxon at site i in half century h.

* $\alpha_h$: half century fixed effect.

* $c_i$: site fixed effect.

* $\text{post}_h$: indicator for Archaic and Classical periods.

* $d_i$: measure of trade cost between site i and Sigeion (ancient Greek city-state at Aegean entrance to Dardanelles).

* Costs of trade influenced site-specific cultivation choices in Archaic and Classical periods: when trade cost (distance) increases, more cultivation of cereals and less cash crops.
## Difference-in-differences estimates

<table><thead><tr><th></th><th>log(Cereals+1)</th><th>log(Olive+1)</th><th>log(Vine+1)</th></tr></thead><tbody><tr><td>ORBIS model</td><td></td><td></td><td></td></tr><tr><td>log(Trade costs)</td><td>2.483**<br>(0.725)</td><td>-2.956**<br>(0.669)</td><td>-0.505**<br>(0.082)</td></tr><tr><td>Observations</td><td>37</td><td>37</td><td>37</td></tr><tr><td>G</td><td>6</td><td>6</td><td>6</td></tr><tr><td>G*</td><td>2.698</td><td>2.698</td><td>2.698</td></tr><tr><td>R<sup>2</sup></td><td>0.539</td><td>0.617</td><td>0.347</td></tr></tbody></table>

Source: Izdebski et al. (2020)
* Tree rings (dendrochronological dating):

* Width of each ring indicates growth conditions in year when formed: proxy for temperature or rainfall.

* International Tree Ring Data Bank: data for 4,000 sites on six continents.

* Also timbers from historical buildings can be used to date them.
Ljungqvist et al. (2018)

* How do plagues affect economic activity? Focus on 1250–1699 Europe.

* Written records exist, but not well preserved and spatially biased.

* Analyze link between construction activity (proxied by tree felling dates) and plague outbreaks.
* Dendroarcheological data:

* 49,640 data points in Austria, Belgium, Czech Republic, France, Germany, Switzerland.

* Focus on tree-ring sequences where terminal ring of last year of tree growth (waney edge) is present ⇒ get exact felling year of tree.

* Wood in dry conditions in buildings' roofs, ceilings, and basement pillars.
Tree rings

<figure><img src="image_179.png" /></figure>

Source: Ljungqvist et al. (2018)
Examples of constructions in dendroarcheological dataset

<figure><img src="image_180.png" /></figure>

Source: Ljungqvist et al. (2018)
* Plague outbreaks:

* 9,772 plague outbreaks with year and location information in 1346–1699. Biraben (1975) Bütgen et al. (2012)

* Issues of overlap between plagues and tree felling dates.

* Normalize data by netting out 10-year averages and standardizing values to focus on deviations from trends.
<figure><img src="image_181.png" /><figcaption>Locations of felling dates and plague outbreaks</figcaption></figure>

Source: Ljungqvist et al. (2018)
Tree rings

<figure><img src="image_182.png" /></figure>

Source: Ljungqvist et al. (2018)
<figure><img src="image_183.png" /><figcaption>Standardized number of felling dates</figcaption></figure>

Source: Ljungqvist et al. (2018)
Standardized number of plague outbreaks

<figure><img src="image_184.png" /></figure>

Source: Ljungqvist et al. (2018)
<figure><img src="image_185.png" /><figcaption>Plague outbreaks and felling dates</figcaption></figure>

Source: Ljungqvist et al. (2018)
* Bones are easily preserved and data can be extracted from them:

* Height, even with incomplete skeleton.

* Age at death before 20, especially if skull preserved (using patterns of dentition).

* Gender with various methodologies.

* Presence of specific diseases, starvation, violent death.

* Important dataset for North America: the Western Hemisphere Project, with 12k observations across 65 sites for over 8k years.
Stock et al. (2023)

* Was the Neolithic Revolution detrimental to health conditions?

* Neolithic Revolution: transition from hunting and gathering to agriculture subsistence 8–10 kya (kilo years ago).

* Surprisingly, associated with worse general health (systematic evidence).

* Lower height and body mass from skeletal data.

* Main reasons: initial increase in food availability dominated by higher population density, sedentism, infectious diseases, and zoonotic diseases associated with domestic animals.
* But transition less rapid and dichotomous than thought:

* Mixed strategies of plant domestication and foraging lasted for hundred years.

* Evidence of declines in stature before transition.

* Evidence of recoveries in some regions.
* But transition less rapid and dichotomous than thought:

* Mixed strategies of plant domestication and foraging lasted for hundred years.

* Evidence of declines in stature before transition.

* Evidence of recoveries in some regions.

* Concomitant Secondary Products Revolution:

* Use of domestic animals for byproducts (milk, wool, labor).

* Barrier to milk consumption: inability to digest milk sugar lactose after childhood.

* Some populations exhibit adaptive response to cultural shift toward dairying (Europe and East Africa).

* Evidence that milk consumption promote skeletal growth.
* Why interesting for economic history?

* Starting point of settlements, institutions, and States.

* Agriculture invented independently multiple times, so causal inference possible.

* Places that transitioned earlier are wealthier today: deep roots of comparative development (evidence in Topic 2.6).
* Most recent and comprehensive evidence based on skeletal data:

* Explore association of reduction in body size and transition to agriculture.

* Heterogeneity across regions depending on domestication.

* Recovery where selection for lactose persistence?
* Agriculture and domestication of animals originated independently in different regions at different times throughout first half of Holocene.

* Could occur through two factors: cultural change and/or demic diffusion (migration of farmers in new territories).

* Levant region: signs of transition 14.5-11.5 kya, full swing by 9 kya.

* Southern Europe: rapid transition 8-7.5 kya driven by demic diffusion and cultural change by hunter-gatherer populations.

* Central Europe: more gradual, driven by demic diffusion from Balkans.

* Northern Europe: adoption limited by climatic factors and limited contacts 7.4-4 kya, through cultural change.

* Britain: also late adoption, but through demic diffusion from continental farmers.
* Similarly heterogeneous diffusion of dairying patterns:

* Data: lipid residues in Neolithic pottery and genetic variation.

* Earliest evidence from Northern Europe 5 kya.

* Evidence of genetic variants in Central and Northern Europe associated with demic diffusion.

* Potentially due to challenges of establishing crop species at higher latitudes and increased prevalence of zoonotic diseases.
* Skeletal data:

* 3,507 individual skeletons from 366 archaeological sites in Levant, Europe, Nile Valley, South Asia, China from 34 kya to present.

* Stature derived from maximum femur and tibial length.

* Body mass derived from femoral head diameters and maximum breadth of tibial plateau.

* Date skeletons by radiocarbon, grouped into millennial cohorts.
## Location of archaeological sites

<figure><img src="image_186.png" /></figure>

Source: Stock et al. (2023)
## Location of archaeological sites

<figure><img src="image_187.png" /></figure>

Source: Stock et al. (2023)
# Skeletal data

## Location of archaeological sites

<figure><img src="image_188.png" /></figure>
Location of archaeological sites

<figure><img src="image_189.png" /></figure>

Source: Stock et al. (2023)
Stature (cm) variation worldwide

<figure><img src="image_190.png" /></figure>

Source: Stock et al. (2023)
<figure><img src="image_191.png" /><figcaption>Body mass (kg) variation worldwide</figcaption></figure>

Source: Stock et al. (2023)
## Stature (cm) variation in the Levant

<figure><img src="image_192.png" /></figure>

Source: Stock et al. (2023)
## Body mass (kg) variation in the Levant

<figure><img src="image_193.png" /></figure>

Source: Stock et al. (2023)
# Skeletal data

<figure><img src="image_194.png" /><figcaption>Stature (cm) variation in Europe</figcaption></figure>

Source: Stock et al. (2023)
## Body mass (kg) variation in Europe

<figure><img src="image_195.png" /></figure>

Source: Stock et al. (2023)
Stature (cm) variation in Nile, South Asia, China

<figure><img src="image_196.png" /></figure>

Source: Stock et al. (2023)
<figure><img src="image_197.png" /><figcaption>Body mass (kg) variation in Nile, South Asia, China</figcaption></figure>

Source: Stock et al. (2023)
* Results from pooled data: statures reach minimum 8–6 kya, followed by rebounds.

* Heterogeneous trends on regional scales.

* Levant, where earliest evidence of transition to agriculture: declines in make stature 20–9 kya, and general stability until 3 kya.

* Southern Europe: decline 10–6 kya then rebound.

* Central Europe: increase 8–5 kya.

* Northern Europe: increase in stature from 7 kya and decline in body mass until 5 kya and rebound.

* Heterogeneous trends in other regions.

* Most significant long-term increases in Central and Northern Europe where evidence for selection on lactose persistence.
# Skeletal data

<figure><img src="image_198.png" /><figcaption>Stature (cm) and body mass (kg) variation in Europe</figcaption></figure>

Source: Stock et al. (2023)
<figure><img src="image_199.png" /><figcaption>Stature (cm) variation in Europe</figcaption></figure>
## Body mass (kg) variation in Europe

<figure><img src="image_200.png" /></figure>
* Buildings are generally well preserved and are indicators of historical human activity.

* Archaeological sites of settlement remains.

* Ancient road networks connecting urban networks.
Bakker et al. (2021)

* How did trading opportunities affect economic development in the Bronze and Iron Age?

* Focus on the first massive expansion in maritime trade: the crossing of the Mediterranean at the time of the Phoenicians from 900 BC.

* Capture trading opportunities by connectedness of points along the coast.

* Relate connectedness to early development, measured by presence of archaeological sites.

* Find that locational advantages for sea trade helped development of the Mediterranean region through 1 AD.
* The geography of the Mediterranean:

* Mediterranean: large inland sea protected from open oceans by Strait of Gibraltar.

* Favorable climatic conditions for agriculture in the North.

* Uneven distribution of natural resources and climate → opportunities for trade to exploit comparative advantage.
* Ancient seafaring in the Mediterranean:

* Spread of agriculture from 9500–8000 BC in the Levant to the rest of the region (Iberia in 5500 BC).

* First archaeological evidence of boats from 5500 BC.

* From 1000 BC, evidence of prosperous Phoenician city-states spreading from the Levant throughout the coasts of the Mediterranean.

* Prosperity thanks to trade, with ships crossing 300–700 km of open sea.

* Zone of intense trade and seafaring by Greek, Etruscan, and Phoenician vessels during Classical Antiquity.
* Units of observation:

* Divide Mediterranean and Black sea coastline into 10×10 km grid.

* Grid cell is “coastal” if within 5 km of coastline.

* Dataset: coast (3,352) and land (8,661) cells within 50 km of coast cell.
* Connectedness:

* $d_{ij}$: distance between coastal point i and coastal point j.

* $c_{di}$: number of other coastal cells that can be reached within shipping distance d from cell i (e.g., 500 km).

* $c_d$ is connectivity, capturing trade opportunities.
* Economic development:

* Proxy by number of archaeological sites of settlements in given cell.

* Data from Pleiades Project, providing location, start and end dates of each place.

* Only keep places that stared before 500 AD and categorized as urban or as settlement.
Connectedness for 500 km distance and Pleaiades Narrow Sites, 750 BC

<figure><img src="image_201.png" /></figure>

Source: Bakker et al. (2021). Shares indicate deciles of connectedness. Darker points show better-connected cells.
* Estimate OLS regressions of the form:

$$u_{it} = \beta_{dt}c_{di} + X_{i}\gamma_{t} + e_{it}$$

* $u_{it}$: urbanization for grid point $i$ (archaeological sites) in year $t$.

* $c_{di}$: log connectivity for distance $d$ of grid point $i$ (closest coastal cell if inland cells).

* $X_{i}$: vector of grid point control variables.

* Cluster standard errors at grid level 200×200 km.

* Controls:

* Latitude and longitude: captures climatic conditions.

* Indicator variable for type of cell (coast or inland).

* Distance from Fertile Crescent (origin of spread of agriculture through Mediterranean).
* Estimate OLS regressions of the form:

$$u_{it} = \beta_{dt}c_{di} + X_{i}\gamma_{t} + e_{it}$$

* $u_{it}$: urbanization for grid point $i$ (archaeological sites) in year $t$.

* $c_{di}$: log connectivity for distance $d$ of grid point $i$ (closest coastal cell if inland cells).

* $X_{i}$: vector of grid point control variables.

* Cluster standard errors at grid level 200×200 km.

* Controls:

* Latitude and longitude: captures climatic conditions.

* Indicator variable for type of cell (coast or inland).

* Distance from Fertile Crescent (origin of spread of agriculture through Mediterranean).

* Potential outliers: Greek islands (best-connected areas), North Africa (lack of archaeological exploration).
## Balancing checks

<table><thead><tr><th>Dependent Variable</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Agricultural productivity<br>(following Galor & Özak, 2016)</td><td>0.46<br>(0.08)</td><td>0.00<br>(0.10)</td><td>0.54<br>(0.14)</td><td>0.07<br>(0.16)</td><td>0.16<br>(0.11)</td><td>-0.17<br>(0.09)</td></tr><tr><td>Ruggedness<br>(following Nunn & Puga, 2012)</td><td>0.19<br>(0.14)</td><td>0.14<br>(0.19)</td><td>0.06<br>(0.29)</td><td>-0.05<br>(0.28)</td><td>-0.29<br>(0.16)</td><td>-0.13<br>(0.16)</td></tr><tr><td>River proximity</td><td>-3.00<br>(1.72)</td><td>-2.90<br>(2.14)</td><td>-4.32<br>(2.96)</td><td>-3.82<br>(3.33)</td><td>-2.45<br>(2.09)</td><td>-2.99<br>(2.19)</td></tr><tr><td>Mines proximity</td><td>-0.37<br>(0.37)</td><td>0.10<br>(0.74)</td><td>-0.14<br>(1.22)</td><td>0.40<br>(1.48)</td><td>-1.96<br>(0.74)</td><td>-0.04<br>(0.67)</td></tr><tr><td>Wind</td><td>0.31<br>(0.16)</td><td>1.05<br>(0.23)</td><td>-0.53<br>(0.30)</td><td>0.24<br>(0.34)</td><td>0.67<br>(0.17)</td><td>1.20<br>(0.22)</td></tr><tr><td>Land connectedness</td><td>-0.43<br>(0.22)</td><td>-0.30<br>(0.24)</td><td>-0.30<br>(0.15)</td><td>-0.04<br>(0.16)</td><td>-0.24<br>(0.24)</td><td>-0.24<br>(0.24)</td></tr><tr><td>Observations</td><td>12,013</td><td>12,013</td><td>10,064</td><td>10,064</td><td>9,464</td><td>9,464</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Longitude and latitude</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Distance to coast and Fertile Crescent</td><td></td><td>X</td><td></td><td>X</td><td></td><td>X</td></tr><tr><td>Dropping Aegean</td><td></td><td></td><td>X</td><td>X</td><td></td><td></td></tr><tr><td>Dropping North Africa</td><td></td><td></td><td></td><td></td><td>X</td><td>X</td></tr></tbody></table>

Source: Bakker et al. (2021). Each coefficient is from a different regression. The regressor is log connectedness.
Main result (in 750 BC)

<table><thead><tr><th>Dependent Variable</th><th>Dependent Variable Mean</th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>A. Basic results</td><td></td><td></td><td></td><td></td></tr><tr><td>Pleiades wide 750 BC</td><td>0.130</td><td>0.208<br>(0.056)</td><td>0.103<br>(0.044)</td><td>0.204<br>(0.056)</td></tr><tr><td>Pleiades narrow 750 BC</td><td>0.103</td><td>0.156<br>(0.048)</td><td>0.075<br>(0.035)</td><td>0.156<br>(0.048)</td></tr><tr><td>Observations</td><td>12,013</td><td>12,013</td><td>10,064</td><td>9,464</td></tr><tr><td>B. Results excluding coastal cells from outcome definition</td><td></td><td></td><td></td><td></td></tr><tr><td>Pleiades wide 750 BC</td><td>0.100</td><td>0.176<br>(0.063)</td><td>0.095<br>(0.048)</td><td>0.184<br>(0.062)</td></tr><tr><td>Pleiades narrow 750 BC</td><td>0.081</td><td>0.131<br>(0.053)</td><td>0.073<br>(0.041)</td><td>0.140<br>(0.053)</td></tr><tr><td>Observations</td><td>8,661</td><td>8,661</td><td>7,567</td><td>6,647</td></tr><tr><td>C. Results excluding short connections</td><td></td><td></td><td></td><td></td></tr><tr><td>Pleiades wide 750 BC</td><td>0.130</td><td>0.201<br>(0.052)</td><td>0.103<br>(0.042)</td><td>0.197<br>(0.053)</td></tr><tr><td>Pleiades narrow 750 BC</td><td>0.103</td><td>0.152<br>(0.045)</td><td>0.076<br>(0.034)</td><td>0.152<br>(0.045)</td></tr><tr><td>Observations</td><td>12,013</td><td>12,013</td><td>10,064</td><td>9,464</td></tr><tr><td>Controls</td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>Dropping Aegean</td><td></td><td></td><td>X</td><td>X</td></tr><tr><td>Dropping North Africa</td><td></td><td></td><td></td><td>X</td></tr></tbody></table>

Coefficients from regressions of the number of sites on 500 km log connectedness and controls. Controls include longitude, latitude, distance to the coast, and distance to the Fertile Crescent. The dependent variable counts the number of sites in a cell based on either the wide or the narrow Pleiades measure. Panel B excludes coastal cells from the sample, and panel C excludes log connectedness from 100 to 500 km as main regressor. Standard errors are clustered at the level of 200 × 200 km cells, in parentheses.
* Evidence that trade opportunities ⇒ early development in 750 BC.

* ↑ connectedness by 10% ⇒ ↑ archaeological sites by 0.02 (compared to mean of 0.130. So elasticity above 1.

* Dropping Greek islands decreases magnitude.

* Dropping coastal cells (hence, harbors) does not change results.

* Dropping short connections (100 km) does not change results, hence long-distance trade matters.
<figure><img src="image_202.png" /><figcaption>Scaled coefficients for different years</figcaption></figure>

Source: Bakker et al. (2021). Outcome is scaled by dividing by average number of sites in each year, turning it into elasticity.
* Results from 500 year-specific coefficients:

* Relationship between settlements and connections emerges during middle Bronze Age (2000 BC), though not precise.

* Most intense relationship during Phoenician trade period from 1000 BC to end of Roman Empire
Delgaard et al. (2022)

* What is the effect of historical transport infrastructures on long-term economic development?

* Focus on Roman roads network as of the 2nd century.

* Initially built with military purpose, in straight lines, when little roads present.

* Persistence of road infrastructure: higher Roman roads density during antiquity ⇒ higher road density today.

* Strong vector of economic development in the past and today.
* Units of observation:

* Area within the border of the Roman Empire in 117 CE (maximum territorial expansion).

* Divide Empire into cells of 1×1 degrees of latitude by longitude (a little more than 100×100 km).

* Keep only cells treated by at least one Roman road.
* Units of observation:

* Area within the border of the Roman Empire in 117 CE (maximum territorial expansion).

* Divide Empire into cells of 1×1 degrees of latitude by longitude (a little more than 100×100 km).

* Keep only cells treated by at least one Roman road.

* Roman roads:

* Based on the Barrington Atlas of the Greek and Roman World, itself based on archaeological remains.

* Draw 5 km buffer around each road and compute percentage of area within a grid cell.
* Outcome 1: modern roads in 2021 from ESRI.

* Outcome 2: roman settlements in 500 CE from the Digital Atlas of the Roman Empire.

* Outcome 3: average intensity of lights at night 2013–20 from the Earth Observation Group.

* Outcome 4: “gridded” population density in 2010 from the UN.
Extract from the Barrington Atlas

<figure><img src="image_203.png" /></figure>
<figure><img src="image_204.png" /><figcaption>## Roman roads and contemporary night light intensity</figcaption></figure>

Source: Delgaard et al. (2022).
<figure><img src="image_205.png" /><figcaption>## Roman roads and contemporary night light intensity</figcaption></figure>

Source: Delgaard et al. (2022).
* Estimate OLS regressions of the form:

$$ \log(y_{grc}) = \delta_c + \delta_r + \beta \cdot \text{RDD}_{grc} + X'_{grc}\gamma + \varepsilon_{grc} $$

* $y_{grc}$: outcome of grid cell $g$ in language region $r$ and country $c$.

* $\delta_c$ and $\delta_r$: language region and country fixed effects to control for cross-country and cultural variation that might affect economic activity.

* $\text{RDD}_{grc}$: log of roman roads density.

* $X'_{grc}$: vector of cell control variables.

* Adjust standard errors for spatial autocorrelation up to 5 grid cells (361 km).

* Controls:

* Geographic controls: latitude, longitude, ruggedness, elevation, soil quality.

* Proximity to water bodies (coast, rivers, natural harbors).

* Distances to border of empire and current capital.
<table><thead><tr><th colspan="8">Dependent variable: Modern roads</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>In (1+) Roman roads</td><td>0.331<sup>***</sup><br>(0.055)</td><td>0.240<sup>***</sup><br>(0.058)</td><td>0.227<sup>**</sup><br>(0.062)</td><td>0.115<sup>*</sup><br>(0.063)</td><td>0.203<sup>***</sup><br>(0.062)</td><td>0.214<sup>***</sup><br>(0.063)</td><td>0.119<sup>*</sup><br>(0.065)</td></tr><tr><td>In Area</td><td>-0.003<br>(0.006)</td><td>0.006<br>(0.007)</td><td>0.005<br>(0.009)</td><td>0.009<br>(0.008)</td><td>0.008<br>(0.009)</td><td>0.005<br>(0.009)</td><td>0.008<br>(0.009)</td></tr><tr><td>In Latitude</td><td>0.322<sup>***</sup><br>(0.043)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (10+) Longitude</td><td>-0.013<br>(0.039)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In Longitude squared</td><td>0.002<br>(0.008)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (50+) Elevation</td><td></td><td></td><td>-0.051<sup>***</sup><br>(0.011)</td><td></td><td></td><td></td><td>-0.049<sup>***</sup><br>(0.012)</td></tr><tr><td>In Post-1500 caloric<br>suitability</td><td></td><td></td><td>0.012<sup>**</sup><br>(0.005)</td><td></td><td></td><td></td><td>0.011<sup>**</sup><br>(0.005)</td></tr><tr><td>In Distance to coast</td><td></td><td></td><td></td><td></td><td>-0.010<br>(0.008)</td><td></td><td>0.001<br>(0.008)</td></tr><tr><td>In Distance to major<br>river</td><td></td><td></td><td></td><td></td><td>-0.006<br>(0.006)</td><td></td><td>0.002<br>(0.006)</td></tr><tr><td>In Distance to<br>natural harbor</td><td></td><td></td><td></td><td></td><td>-0.011<sup>**</sup><br>(0.005)</td><td></td><td>-0.012<sup>**</sup><br>(0.006)</td></tr><tr><td>In Distance to Roman<br>Empire border</td><td></td><td></td><td></td><td></td><td></td><td>-0.007<br>(0.013)</td><td>-0.004<br>(0.013)</td></tr><tr><td>In (1+) Number of<br>mines</td><td></td><td></td><td></td><td></td><td></td><td>-0.003<br>(0.008)</td><td>-0.001<br>(0.008)</td></tr><tr><td>In Distance to<br>capital</td><td></td><td></td><td></td><td></td><td></td><td>-0.013<br>(0.010)</td><td>-0.009<br>(0.010)</td></tr><tr><td>In (1+) Distance to<br>coal</td><td></td><td></td><td></td><td></td><td></td><td>0.003<br>(0.005)</td><td>-0.003<br>(0.005)</td></tr><tr><td>In Distance to<br>university</td><td></td><td></td><td></td><td></td><td></td><td>-0.008<br>(0.011)</td><td>0.000<br>(0.011)</td></tr><tr><td>In Distance to<br>Wittenberg</td><td></td><td></td><td></td><td></td><td></td><td>-0.026<br>(0.054)</td><td>-0.044<br>(0.051)</td></tr><tr><td>Country FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-language FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td></tr><tr><td>R<sup>2</sup></td><td>0.232</td><td>0.391</td><td>0.475</td><td>0.515</td><td>0.488</td><td>0.481</td><td>0.524</td></tr></tbody></table>

Source: Delgaard et al. (2022).
Conditional relationship between modern roads and Roman roads

<figure><img src="image_206.png" /></figure>

Source: Delgaard et al. (2022).
<table><thead><tr><th></th><th colspan="7">Dependent variable: Roman settlements in 500 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>In (1+) Roman roads</td><td>1.096<sup>**</sup><br>(0.169)</td><td>0.811<sup>***</sup><br>(0.178)</td><td>0.832<sup>**</sup><br>(0.189)</td><td>0.577<sup>***</sup><br>(0.203)</td><td>0.728<sup>**</sup><br>(0.195)</td><td>0.778<sup>**</sup><br>(0.187)</td><td>0.499<sup>**</sup><br>(0.205)</td></tr><tr><td>In Area</td><td>0.178<sup>**</sup><br>(0.019)</td><td>0.171<sup>***</sup><br>(0.021)</td><td>0.199<sup>***</sup><br>(0.017)</td><td>0.205<sup>***</sup><br>(0.018)</td><td>0.176<sup>**</sup><br>(0.019)</td><td>0.186<sup>**</sup><br>(0.017)</td><td>0.200<sup>***</sup><br>(0.020)</td></tr><tr><td>In Latitude</td><td>-0.813<sup>***</sup><br>(0.174)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (10+) Longitude</td><td>-0.118<br>(0.126)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In Longitude squared</td><td>0.037<br>(0.027)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (50+) Elevation</td><td></td><td></td><td>-0.107<sup>***</sup><br>(0.041)</td><td></td><td></td><td></td><td>-0.069<br>(0.045)</td></tr><tr><td>In Pre-1500 caloric<br>suitability</td><td></td><td></td><td>0.037<sup>**</sup><br>(0.018)</td><td></td><td></td><td></td><td>0.039<sup>**</sup><br>(0.017)</td></tr><tr><td>In Distance to coast</td><td></td><td></td><td></td><td>-0.062<sup>***</sup><br>(0.023)</td><td></td><td></td><td>-0.039<br>(0.026)</td></tr><tr><td>In Distance to major<br>river</td><td></td><td></td><td></td><td></td><td>-0.027<br>(0.026)</td><td></td><td>-0.018<br>(0.027)</td></tr><tr><td>In Distance to<br>natural harbor</td><td></td><td></td><td></td><td></td><td>-0.053<sup>**</sup><br>(0.024)</td><td></td><td>-0.058<sup>**</sup><br>(0.024)</td></tr><tr><td>In Distance to Roman<br>Empire border</td><td></td><td></td><td></td><td></td><td></td><td>0.014<br>(0.028)</td><td>-0.004<br>(0.030)</td></tr><tr><td>In (1+) Number of<br>mines</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.115<sup>**</sup><br>(0.046)</td></tr><tr><td>Country FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-language FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td></tr><tr><td>R<sup>2</sup></td><td>0.189</td><td>0.324</td><td>0.394</td><td>0.407</td><td>0.416</td><td>0.401</td><td>0.430</td></tr></tbody></table>

Source: Delgaard et al. (2022).
<table><thead><tr><th></th><th colspan="7">Dependent variable: Nightlights from 2013-2020</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>In (1+) Roman roads</td><td>1.509<sup>***</sup><br>(0.193)</td><td>1.346<sup>***</sup><br>(0.178)</td><td>1.199<sup>***</sup><br>(0.166)</td><td>0.714<sup>**</sup><br>(0.164)</td><td>1.032<sup>***</sup><br>(0.162)</td><td>1.094<sup>***</sup><br>(0.163)</td><td>0.675<sup>***</sup><br>(0.158)</td></tr><tr><td>In Area</td><td>-0.040<br>(0.029)</td><td>-0.011<br>(0.025)</td><td>-0.010<br>(0.023)</td><td>0.026<br>(0.023)</td><td>0.023<br>(0.023)</td><td>-0.025<br>(0.028)</td><td>0.029<br>(0.026)</td></tr><tr><td>In Latitude</td><td>-0.412<sup>***</sup><br>(0.195)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (10+) Longitude</td><td>-0.099<br>(0.214)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In Longitude squared</td><td>-0.010<br>(0.041)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (50+) Elevation</td><td></td><td></td><td>-0.290<sup>***</sup><br>(0.034)</td><td></td><td></td><td></td><td>-0.232<sup>***</sup><br>(0.038)</td></tr><tr><td>In Post-1500 caloric<br>stability</td><td></td><td></td><td>0.017<br>(0.016)</td><td></td><td></td><td></td><td>-0.003<br>(0.016)</td></tr><tr><td>In Distance to coast</td><td></td><td></td><td></td><td>-0.101<sup>***</sup><br>(0.025)</td><td></td><td></td><td>-0.067<sup>***</sup><br>(0.028)</td></tr><tr><td>In Distance to major<br>river</td><td></td><td></td><td></td><td>-0.041<sup>***</sup><br>(0.021)</td><td></td><td></td><td>-0.001<br>(0.020)</td></tr><tr><td>In Distance to<br>natural harbor</td><td></td><td></td><td></td><td>-0.046<sup>***</sup><br>(0.020)</td><td></td><td></td><td>-0.043<sup>***</sup><br>(0.019)</td></tr><tr><td>In Distance to Roman<br>Empire border</td><td></td><td></td><td></td><td></td><td>-0.006<br>(0.042)</td><td></td><td>-0.017<br>(0.041)</td></tr><tr><td>In (1+) Number of<br>mines</td><td></td><td></td><td></td><td></td><td>-0.004<br>(0.031)</td><td></td><td>0.006<br>(0.029)</td></tr><tr><td>In Distance to<br>capital</td><td></td><td></td><td></td><td></td><td>-0.191<sup>***</sup><br>(0.047)</td><td></td><td>-0.197<sup>***</sup><br>(0.048)</td></tr><tr><td>In (1+) Distance to<br>coal</td><td></td><td></td><td></td><td></td><td>-0.002<br>(0.017)</td><td></td><td>-0.030<sup>***</sup><br>(0.016)</td></tr><tr><td>In Distance to<br>university</td><td></td><td></td><td></td><td></td><td>-0.052<br>(0.043)</td><td></td><td>-0.018<br>(0.041)</td></tr><tr><td>In Distance to<br>Wittenberg</td><td></td><td></td><td></td><td></td><td>-0.127<br>(0.184)</td><td></td><td>-0.302<sup>***</sup><br>(0.167)</td></tr><tr><td>Country FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-language FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>691</td><td>691</td><td>691</td><td>691</td><td>691</td><td>691</td><td>691</td></tr><tr><td>R<sup>2</sup></td><td>0.179</td><td>0.465</td><td>0.562</td><td>0.621</td><td>0.598</td><td>0.597</td><td>0.668</td></tr></tbody></table>

Source: Delgaard et al. (2022).
Conditional relationship between modern nightlights and Roman roads

<figure><img src="image_207.png" /></figure>

Source: Delgaard et al. (2022).
<table><thead><tr><th></th><th colspan="7">Dependent variable: Population in 2010</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>In (1+) Roman roads</td><td>3.017<sup>***</sup><br>(0.414)</td><td>2.638<sup>***</sup><br>(0.426)</td><td>2.624<sup>***</sup><br>(0.396)</td><td>1.215<sup>***</sup><br>(0.345)</td><td>2.246<sup>***</sup><br>(0.375)</td><td>2.464<sup>***</sup><br>(0.376)</td><td>1.160<sup>***</sup><br>(0.326)</td></tr><tr><td>In Area</td><td>1.002<sup>**</sup><br>(0.064)</td><td>1.029<sup>**</sup><br>(0.069)</td><td>1.099<sup>***</sup><br>(0.039)</td><td>1.115<sup>***</sup><br>(0.039)</td><td>1.171<sup>***</sup><br>(0.037)</td><td>1.060<sup>***</sup><br>(0.040)</td><td>1.115<sup>***</sup><br>(0.038)</td></tr><tr><td>In Latitude</td><td>2.292<sup>**</sup><br>(0.507)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (10+) Longitude</td><td>-0.395<br>(0.347)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In Longitude squared</td><td>0.055<br>(0.070)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In (50+) Elevation</td><td></td><td></td><td>-0.510<sup>***</sup><br>(0.076)</td><td></td><td></td><td></td><td>-0.431<sup>***</sup><br>(0.082)</td></tr><tr><td>In Post-1500 caloric<br>suitability</td><td></td><td></td><td>0.246<sup>***</sup><br>(0.056)</td><td></td><td></td><td></td><td>0.214<sup>***</sup><br>(0.053)</td></tr><tr><td>In Distance to coast</td><td></td><td></td><td></td><td>-0.224<sup>***</sup><br>(0.055)</td><td></td><td></td><td>-0.117<sup>**</sup><br>(0.054)</td></tr><tr><td>In Distance to major<br>river</td><td></td><td></td><td></td><td></td><td>-0.113<sup>***</sup><br>(0.048)</td><td></td><td>-0.036<br>(0.049)</td></tr><tr><td>In Distance to<br>natural harbor</td><td></td><td></td><td></td><td>-0.078<sup>***</sup><br>(0.046)</td><td></td><td></td><td>-0.088<sup>***</sup><br>(0.040)</td></tr><tr><td>In Distance to Roman<br>Empire border</td><td></td><td></td><td></td><td></td><td>0.075<br>(0.079)</td><td></td><td>0.077<br>(0.072)</td></tr><tr><td>In (1+) Number of<br>mines</td><td></td><td></td><td></td><td></td><td>-0.062<br>(0.061)</td><td></td><td>-0.046<br>(0.055)</td></tr><tr><td>In Distance to<br>capital</td><td></td><td></td><td></td><td></td><td>-0.461<sup>***</sup><br>(0.079)</td><td></td><td>-0.390<sup>***</sup><br>(0.075)</td></tr><tr><td>In (1+) Distance to<br>coal</td><td></td><td></td><td></td><td></td><td>-0.032<br>(0.033)</td><td></td><td>-0.094<sup>***</sup><br>(0.030)</td></tr><tr><td>In Distance to<br>university</td><td></td><td></td><td></td><td></td><td>-0.058<br>(0.075)</td><td></td><td>-0.042<br>(0.079)</td></tr><tr><td>In Distance to<br>Wittenberg</td><td></td><td></td><td></td><td></td><td>-0.483<br>(0.324)</td><td></td><td>-0.726<sup>***</sup><br>(0.316)</td></tr><tr><td>Country FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-language FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td><td>693</td></tr><tr><td>R<sup>2</sup></td><td>0.593</td><td>0.701</td><td>0.769</td><td>0.812</td><td>0.784</td><td>0.787</td><td>0.829</td></tr></tbody></table>

Source: Delgaard et al. (2022).
Conditional relationship between modern population and Roman roads

<figure><img src="image_208.png" /></figure>

Source: Delgaard et al. (2022).
* Main findings: strong persistence of Roman road network and past and modern economic development.
* Main findings: strong persistence of Roman road network and past and modern economic development.

* Causal? Use abandonment of wheeled transport in MENA region after 500 BCE.

* Substitution from wheel transport with camel caravans as more efficient in the region.

* Roman roads fell into disrepair compared to Europe, where continued use.

* When automobile arrived in MENA, road planning differed from Roman roads (which had disappeared).

* Absence of relation in MENA after abandonment of wheel supports causal interpretation.
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Settlements in 500 CE</th><th colspan="2">Modern roads</th><th colspan="2">Nights from 2010-2013</th><th colspan="2">Population in 2010</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><tr><th>Sample:</th><th>Europe</th><th>MENA</th><th>Europe</th><th>MENA</th><th>Europe</th><th>MENA</th><th>Europe</th><th>MENA</th></tr></thead><tbody><tr><td>In (1+) Roman roads</td><td>0.470<sup>***</sup><br>(0.255)</td><td>0.599<sup>***</sup><br>(0.358)</td><td>0.208<sup>***</sup><br>(0.072)</td><td>-0.115<br>(0.091)</td><td>0.783<sup>***</sup><br>(0.191)</td><td>0.405<br>(0.282)</td><td>1.405<sup>***</sup><br>(0.390)</td><td>0.717<br>(0.630)</td></tr><tr><td>In Area</td><td>0.171<sup>***</sup><br>(0.204<sup>***</sup>)</td><td>0.371<sup>***</sup><br>(0.235<sup>***</sup>)</td><td>0.084<sup>***</sup><br>(0.026<sup>***</sup>)</td><td>0.071<sup>***</sup><br>(0.051<sup>***</sup>)</td><td>0.155<sup>***</sup><br>(0.071<sup>***</sup>)</td><td>0.369<sup>***</sup><br>(0.069<sup>***</sup>)</td><td>1.180<sup>***</sup><br>(0.035<sup>***</sup>)</td><td>1.045<sup>***</sup><br>(0.096<sup>***</sup>)</td></tr><tr><td>In (50+) Elevation</td><td>-0.090<sup>***</sup><br>(0.054)</td><td>-0.060<sup>***</sup><br>(0.096)</td><td>-0.061<sup>***</sup><br>(0.015)</td><td>-0.011<br>(0.021)</td><td>-0.226<sup>***</sup><br>(0.046)</td><td>-0.300<sup>***</sup><br>(0.078)</td><td>-0.456<sup>***</sup><br>(0.089)</td><td>-0.587<sup>***</sup><br>(0.206)</td></tr><tr><td>In Pre-1500 caloric suitability</td><td>-0.012<br>(0.018)</td><td>0.051<sup>***</sup><br>(0.020)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In Distance to coast</td><td>-0.013<br>(0.030)</td><td>-0.095<sup>***</sup><br>(0.057)</td><td>0.010<br>(0.011)</td><td>0.006<br>(0.011)</td><td>-0.081<sup>***</sup><br>(0.038)</td><td>-0.021<br>(0.047)</td><td>-0.103<sup>***</sup><br>(0.054)</td><td>-0.101<br>(0.125)</td></tr><tr><td>In Distance to major river</td><td>-0.000<br>(0.027)</td><td>-0.034<br>(0.068)</td><td>0.010<br>(0.007)</td><td>0.005<br>(0.010)</td><td>0.034<br>(0.023)</td><td>-0.058<br>(0.042)</td><td>0.073<br>(0.048)</td><td>-0.306<sup>***</sup><br>(0.111)</td></tr><tr><td>In Distance to natural harbor</td><td>-0.030<br>(0.031)</td><td>-0.071<sup>***</sup><br>(0.040)</td><td>-0.014<br>(0.009)</td><td>-0.018<sup>***</sup><br>(0.007)</td><td>-0.028<br>(0.027)</td><td>-0.104<sup>***</sup><br>(0.030)</td><td>-0.060<br>(0.051)</td><td>-0.146<sup>***</sup><br>(0.081)</td></tr><tr><td>In Distance to Roman University</td><td>0.011<br>(0.031)</td><td>0.077<br>(0.292)</td><td>-0.008<br>(0.013)</td><td>-0.141<br>(0.099)</td><td>-0.042<br>(0.044)</td><td>-0.494<br>(0.463)</td><td>0.033<br>(0.076)</td><td>0.195<br>(0.940)</td></tr><tr><td>In (1-) Number of mines</td><td>0.071<br>(0.045)</td><td>0.394<sup>***</sup><br>(0.167)</td><td>-0.008<br>(0.008)</td><td>0.027<br>(0.022)</td><td>-0.021<br>(0.031)</td><td>0.148<br>(0.095)</td><td>-0.095<sup>***</sup><br>(0.056)</td><td>0.143<br>(0.182)</td></tr><tr><td>In Post-1500 caloric suitability</td><td></td><td></td><td>0.011<sup>***</sup><br>(0.006)</td><td>0.017<sup>***</sup><br>(0.006)</td><td>-0.005<br>(0.023)</td><td>0.013<br>(0.019)</td><td>0.115<sup>***</sup><br>(0.039)</td><td>0.240<sup>***</sup><br>(0.080)</td></tr><tr><td>In Distance to capital</td><td></td><td></td><td>-0.013<br>(0.012)</td><td>-0.003<br>(0.014)</td><td>-0.178<sup>***</sup><br>(0.063)</td><td>-0.316<sup>***</sup><br>(0.068)</td><td>-0.335<sup>***</sup><br>(0.099)</td><td>-0.667<sup>***</sup><br>(0.162)</td></tr><tr><td>In (1-) Distance to coal</td><td></td><td></td><td>-0.002<br>(0.005)</td><td>-0.002<br>(0.010)</td><td>-0.025<br>(0.016)</td><td>-0.046<br>(0.045)</td><td>-0.085<sup>***</sup><br>(0.029)</td><td>-0.012<br>(0.119)</td></tr><tr><td>In Distance to university</td><td></td><td></td><td>0.006<br>(0.011)</td><td>-0.168<sup>***</sup><br>(0.098)</td><td>-0.035<br>(0.310)</td><td>0.610<sup>***</sup><br>(0.310)</td><td>-0.011<br>(0.074)</td><td>1.041<br>(0.968)</td></tr><tr><td>In Distance to Wittenberg</td><td></td><td></td><td>-0.073<br>(0.055)</td><td>0.671<sup>***</sup><br>(0.280)</td><td>-0.470<sup>***</sup><br>(0.188)</td><td>1.483<br>(1.047)</td><td>-0.862<sup>***</sup><br>(0.329)</td><td>-0.996<br>(2.360)</td></tr><tr><td>County-language FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>468</td><td>225</td><td>468</td><td>225</td><td>466</td><td>225</td><td>468</td><td>225</td></tr><tr><td>R<sup>2</sup></td><td>0.428</td><td>0.409</td><td>0.519</td><td>0.562</td><td>0.700</td><td>0.677</td><td>0.896</td><td>0.729</td></tr></tbody></table>

Source: Delgaard et al. (2022).
* Most sources are written records on paper.

* But archaeological excavations provide written records on other media:

* Coins.

* Tablets.

* Stones with family names.

* Many can be used to study the economic history of the past.
Explaining the Great Divergence

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

<figure><img src="image_211.png" /></figure>

Toulouse
School of
Economics
What factors can explain the Great Divergence?
# The Great Divergence

<figure><img src="image_212.png" /><figcaption>GDP per Capita in Europe and Asia, 1000–1870 (1990 GKI$, Log scale)</figcaption></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015), van Zanden and van Leeuwen (2012), Malanima (2011), Broadberry, Guan and Li (2018), Bassino, Broadberry, Fukao, Gupta and Takashima (2015).
* Neoclassical growth theory.

* Correlates of economic growth.

* Fundamental causes of long-run growth.

* Why did Britain industrialize first?
* Based on the Solow growth model. Solow (1956)

* Micro foundations: neoclassical production function.

* Dynamic general equilibrium model:

* Growth is achieved on math to steady-state.

* Modern growth theory extends to endogenous steady-state growth with technological progress.

* 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.
* 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.
* 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 $K = \bar{K}$.
* 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_213.png" /></figure>
<figure><img src="image_214.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 ($H$).
* Empirically, correlates of economic growth:

* Physical capital accumulation proxied by capital investment rates.

* Human capital accumulation proxied by schooling.

* Technological change proxied by total factor productivity (TFP).

* (Less interested in population since per capita measure)
<figure><img src="image_215.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_216.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_217.png" /><figcaption>Average GDP per Capita Growth and TFP, 1960–2017</figcaption></figure>

Source: Penn World Table 9.1, 71 countries.
* Great 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.
* Great 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.

* Other data from Penn World Tables.
* 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?
* Four common hypotheses:

* Luck: multiple equilibria and path dependence.

* Institutions.

* Culture.

* Geography.
* Four common hypotheses:

* Luck: multiple equilibria and path dependence.

* Institutions.

* Culture.

* Geography.

* Technological change (and K, H) is a consequence of these fundamental causes.

* These causes often interact so challenging to take them separately.

* Institutions seem key condition.
* 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 $\epsilon, \epsilon' > 0$.
* 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.

* Institutions as coordination device for equilibrium selection.
  Myerson (2009)
* Path dependence: remain stuck in inferior equilibrium.

* Institutions as coordination device for equilibrium selection.
  Myerson (2009)

* Luck cannot explain:

* Hundred years of cumulative processes.

* Hundred years of divergence.

* Why some countries can have very rapid growth.
"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.
* Political institutions:

* Inclusive political institutions, e.g., parliaments with equal participation.

* Extractive political institutions, e.g., slavery and exploitation by the few.
* Economic institutions (requiring inclusive political institutions):

* Property rights.

* Functioning markets.

* Contractual opportunities.
* Economic institutions (requiring inclusive political institutions):

* 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 (luck, culture).

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

* Distribution and conflict shape institutions.
* Values, preferences, beliefs matter for economic performance.
* Values, preferences, beliefs matter for economic performance.

* Culture hypothesis version 1: religion, i.e., 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.
* Culture hypothesis version 2: a culture of (non-)cooperation.

* Culture as a coordination mechanism for equilibrium selection.

* Ethno-linguistic fractionalization plays a role.
* Culture hypothesis version 2: a culture of (non-)cooperation.

* Culture as a coordination mechanism for equilibrium selection.

* Ethno-linguistic fractionalization plays a role.

* Difficult to explain growth miracle. How fast is cultural change?
* Culture hypothesis version 3: a culture of growth and useful knowledge.

* Joel Mokyr's (and McCloskey's) view:

* "Scientific Revolution" (17th century) ⇒ "Industrial Enlightenment"

* Large supply of useful knowledge and mechanical skills.

* Knowledge and belief in practical innovation.
* Culture hypothesis version 3: a culture of growth and useful knowledge.

* Joel Mokyr's (and McCloskey's) view:

* "Scientific Revolution" (17th century) ⇒ "Industrial Enlightenment"

* Large supply of useful knowledge and mechanical skills.

* Knowledge and belief in practical innovation.

* Large complementarities with institutions (e.g., contracting and functionning markets).
* 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." [sic]
* Some racist underpinnings...

* 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)*
* Geographic determinism has some truth, but limited explanatory power.
* Geographic determinism has some truth, but limited explanatory power.

* Comparative advantage.

* Low agricultural productivity ⇒ comparative advantage in industry.

* Unfavorable geography provides incentives to industrialize earlier.
* Geographic determinism has some truth, but limited explanatory power.

* 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.
* Geography hypothesis 3: natural endowments.

* Endowments of raw energy material (e.g., coal) to power industrialization.

* True but functioning markets and invention of steam engine preconditions.

* Hence, dependent on institutions.
* Geography hypothesis 4: territorial fragmentation and state building.

* States make wars, wars make states (Tilly).

* Inter-polity conflict generates political institutions.

* Conflict is more prevalent when fragmented geography.

* Political geography is key to explain institutions.
Why did Britain industrialize first?
# Why did Britain industrialize first?

⇒ The “Holy Grail” of economic history

* Overview of main answers for British exceptionalism (1700–1850).
Allen (2009) Blum and Colvin (2018) chap 24, Hoffman (2020), Court (2020)

① Institutions

② A culture of growth and useful knowledge

③ Factor prices and technological change

④ Demography and the European Marriage Pattern

⑤ Trade and colonialism

⑥ Geography and coal
* North and Weingast's long-standing argument.

* Balance of power between sovereign and society through Parliament, solving fundamental commitment problem.

* Secured property rights.

* Enables much higher per capita tax revenues (fiscal capacity).
* North and Weingast's long-standing argument.

* Balance of power between sovereign and society through Parliament, solving fundamental commitment problem.

* Secured property rights.

* Enables much higher per capita tax revenues (fiscal capacity).

* Enables efficient warfare, gains from trade, merchant marine.

* Develop of transportation network (natural monopoly).

* Representative institutions enable urbanization and agglomeration effects.
* Joel Mokyr's (and McCloskey's) view:

* "Scientific Revolution" (17th century) ⇒ "Industrial Enlightenment"

* Large supply of useful knowledge and mechanical skills.

* Knowledge and belief in innovation.

* Abundant supply of useful skills.

* Widespread practice of apprenticeship in Britain.

* Migration patterns of skilled artisans from Britain to continental Europe.

* Inherited protestant culture as well?
* Robert Allen's view:

* High wages, cheap capital and resources (coal).

* Relative factor prices $\Rightarrow$ High demand for innovation.

* Profitable to mechanize and substitute machines for labor.
* Robert Allen's view:

* High wages, cheap capital and resources (coal).

* Relative factor prices $\Rightarrow$ High demand for innovation.

* Profitable to mechanize and substitute machines for labor.

* But recent debates on measurement problem:

* Wage data mostly for daily wage.

* Working hours very different across country, season, region, sector.

* New series show British wages not as high as estimated.
* Unified growth theory links economic growth to demographic changes.

* European Marriage Pattern:

* High female celibacy.

* Late marriage.

* Nuclear families.

* Incentives for human capital investment.
* Unified growth theory links economic growth to demographic changes.

* European Marriage Pattern:

* High female celibacy.

* Late marriage.

* Nuclear families.

* Incentives for human capital investment.

* But very little evidence of link between EMP and growth.
# Trade and colonialism

* British colonies supplied:

* Food: sugar mostly, tea and coffee.

* Raw materials: cotton, timber.

* Was it an essential factor?
* British colonies supplied:

* Food: sugar mostly, tea and coffee.

* Raw materials: cotton, timber.

* Was it an essential factor?

* Assumes only colonies could supply food and materials.

* Assumes imported food was essential.

* Assumes British farmers would have faced diminishing returns in agriculture.
* British colonies supplied:

* Food: sugar mostly, tea and coffee.

* Raw materials: cotton, timber.

* Was it an essential factor?

* Assumes only colonies could supply food and materials.

* Assumes imported food was essential.

* Assumes British farmers would have faced diminishing returns in agriculture.

* Little evidence (from Britain perspective):

* Abundant worldwide supply (slightly higher prices).

* Little share of caloric intake (14% in 1840s).

* British agriculture highly productive.
* Potential advantages of Britain's abundant coal deposits:

* Increasingly important source of heat.

* Mine floods ⇒ incentives for steam engines.

* Main power for textile mines nearby production centers (e.g., Lancashire).
* Potential advantages of Britain's abundant coal deposits:

* Increasingly important source of heat.

* Mine floods ⇒ incentives for steam engines.

* Main power for textile mines nearby production centers (e.g., Lancashire).

* But prevalent in many European locations.

* Ability to exploit and transport coal matters (institutions).
# What can explain the Great Divergence?

## Blum and Colvin (2018) chap 23

* Explanations for “Why Britain” slightly different than for “Why Europe.”
# What can explain the Great Divergence?

## Blum and Colvin (2018) chap 23

* Explanations for “Why Britain” slightly different than for “Why Europe.”

* Applies to both the Little and Great Divergence:

* Institutions and the rules of the game.

* Culture of growth.
# What can explain the Great Divergence?

## Blum and Colvin (2018) chap 23

* Explanations for “Why Britain” slightly different than for “Why Europe.”

* Applies to both the Little and Great Divergence:

* Institutions and the rules of the game.

* Culture of growth.

* Applies to the Great Divergence:

* Warfare and inter-state competition due to land and political fragmentation.
Directed Technical Change

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_218.png" /></figure>

<figure><img src="image_219.png" /></figure>

<figure><img src="image_220.png" /></figure>

Toulouse
School of
Economics
What was the nature of technical change in Britain and
its direct determinants?
<figure><img src="image_221.png" /><figcaption>## Sectoral Annual Average Productivity Growth Rates in Britain</figcaption></figure>

Source: Broadberry (2020) based on Broadberry et al. (2015).
<figure><img src="image_222.png" /><figcaption>Patents per year in England (1660–1851)</figcaption></figure>

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

* Textile sector:

* Hargreaves spinning jenny (mid-1760s)

* Arkwright water frame (late 1760s)

* Crompton mule jenny (late 1770s)

* Steam powered these new machines:

* Newcomen engine (1710s)

* Watt steam engine (1760s)

⇒ Wave of simultaneous innovations that primarily affected the textile (cotton) industry.
<figure><img src="image_223.png" /><figcaption>Newcomen engine (1710s)</figcaption></figure>
<figure><img src="image_224.png" /><figcaption>## Watt steam engine (1760s)</figcaption></figure>
## Hargreave's spinning jenny of 1770

<figure><img src="image_225.png" /></figure>

Source: Allen (2009).
## Hargreave's spinning jenny of 1770

<figure><img src="image_226.png" /></figure>

Source: Allen (2009).
<figure><img src="image_227.png" /><figcaption>## Arkwright's water frame of 1775</figcaption></figure>

Source: Allen (2009).
<figure><img src="image_228.png" /><figcaption>Cotton spinning and weaving productivity (1770–1869)</figcaption></figure>

Source: Clark (2014) based on Harley (1998).
# The cotton industry

* The cotton industry epitomizes the IR in Britain.

* Production growth: 7% per year 1770–1815.

* Total factor productivity growth: 2.6% per year 1780–1860.

* Accounted for 25% of British productivity growth 1780–1860.

* Reduction in output price by factor 10 1750–1820.
# The cotton industry

* The cotton industry epitomizes the IR in Britain.

* Production growth: 7% per year 1770–1815.

* Total factor productivity growth: 2.6% per year 1780–1860.

* Accounted for 25% of British productivity growth 1780–1860.

* Reduction in output price by factor 10 1750–1820.

* Most intensively mechanized sector.

* Large decline in share of labor in value added.

* Many inventions: Hargreave's, spinning jenny, Arkwright water frame, Cartwright loom, Withney cotton gin...

* Biased technical change: save labor.
<figure><img src="image_229.png" /><figcaption>Prices of cotton cloth relative to bread in England</figcaption></figure>

Source: Steinssen (2020) based on Allen (2009).
## Real cost of cotton (16 count) in 1784 prices

<table><thead><tr><th></th><th>Hand Method</th><th>24-Spindle</th><th>Arkwright</th><th>Glasgow</th></tr><tr><th></th><th>1760</th><th>Jenny<br>1775</th><th>Mill<br>1784</th><th>Mill<br>1836</th></tr></thead><tbody><tr><td><strong>Labor:</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Cleaning and Carding</td><td>7.00</td><td>7.00</td><td>2.69</td><td>0.16</td></tr><tr><td>Spinning</td><td>7.00</td><td>2.33</td><td>2.57</td><td>0.34</td></tr><tr><td>Reeling, bundling, etc.</td><td>0.47</td><td>0.47</td><td>2.19</td><td>0</td></tr><tr><td>Administrative</td><td>2.72</td><td>2.72</td><td>0.41</td><td>0.02</td></tr><tr><td><strong>Total Labor</strong></td><td><strong>17.19</strong></td><td><strong>12.52</strong></td><td><strong>7.86</strong></td><td><strong>0.52</strong></td></tr><tr><td><strong>Materials:</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Raw Cotton</td><td>16.88</td><td>16.88</td><td>16.88</td><td>16.70</td></tr><tr><td>Other</td><td>0</td><td>0</td><td>1.20</td><td>0.53</td></tr><tr><td>Capital</td><td>0.93</td><td>1.88</td><td>2.00</td><td>0.47</td></tr><tr><td><strong>Total Cost</strong></td><td><strong>35.00</strong></td><td><strong>31.28</strong></td><td><strong>27.94</strong></td><td><strong>18.22</strong></td></tr><tr><td><strong>Labor Share of Value Added</strong></td><td><strong>95%</strong></td><td><strong>87%</strong></td><td><strong>80%</strong></td><td><strong>53%</strong></td></tr></tbody></table>

Source: Steinssen (2020) based on Allen (2009).
* Measuring innovation:

* Macro and microinventions

* Technical change in cotton spinning

* Directed technical change
Measuring innovation
## Mokyr (1990)

* Effective characterization of patterns of technical change:

* Microinventions: "small, incremental steps that improve, adapt and streamline existing techniques."

* Macroinventions: "inventions in which a radical new idea, without a clear precedent, emerges more or less ab nihilo."
## Mokyr (1990)

* Effective characterization of patterns of technical change:

* Microinventions: "small, incremental steps that improve, adapt and streamline existing techniques."

* Macroinventions: "inventions in which a radical new idea, without a clear precedent, emerges more or less ab nihilo."

* Traditional measurement of innovations:

* Raw patent counts.

* But do not take into account relative importance of underlying inventions.

⇒ "Patent statistics [in the IR period] do not permit to distinguish between radical and minor inventions"
Nuvolari et al. (2021)

* Composite indicator of quality of the 13,070 English patents 1700–1850.
## Nuvolari et al. (2021)

* Composite indicator of quality of the 13,070 English patents 1700–1850.

* Sources of the “Bibliographic Composite Index of patent quality”

* Woodcroft's *Reference Index* of 1855 on patents 1617–1852: counts number of times each patent mentioned in specialized publications, adjusted for time effects. Nuvolari and Tartari (2011)
    ⇒ Captures visibility of patents in contemporary engineering and legal literature.
* Sources of the "Bibliographic Composite Index of patent quality"

* Number of times patent mentioned in Schookler's (1966) list of "important inventions."

⇒ Captures visibility of patents in modern reference books of history of science and technology.
* Sources of the "Bibliographic Composite Index of patent quality"

* Number of times patent mentioned in Schookler's (1966) list of "important inventions."

⇒ Captures visibility of patents in modern reference books of history of science and technology.

* Number of times patentee mentioned in large set of biographical dictionaries.

⇒ Captures relative visibility of patentee in biographical dictionaries.
Patents and inventors coverage of the sources used to construct patent eminence and inventor eminence

<table><thead><tr><th colspan="3">Patent eminence</th><th colspan="3">Inventor eminence</th></tr><tr><th>Source</th><th>Inventors</th><th>Patents</th><th>Source</th><th>Inventors</th><th>Patents</th></tr></thead><tbody><tr><td>Baker (1976)</td><td>123</td><td>150</td><td>Oxford DNB</td><td>291</td><td>893</td></tr><tr><td>Carter (1978)</td><td>201</td><td>266</td><td>Allen (2009)</td><td>76</td><td>234</td></tr><tr><td>Desmond (1987)</td><td>128</td><td>157</td><td>Day and McNeil (1996)</td><td>240</td><td>708</td></tr><tr><td>Inkster (1991)</td><td>27</td><td>44</td><td>Abbott (1985)</td><td>57</td><td>246</td></tr><tr><td>Dudley (2012)</td><td>33</td><td>55</td><td>Murray (2003)</td><td>54</td><td>199</td></tr><tr><td>Challoner (2009)</td><td>41</td><td>49</td><td>De Galiana (1996)</td><td>102</td><td>333</td></tr><tr><td>Bridgman (2002)</td><td>33</td><td>38</td><td>Meisenzahl and Mokyr (2012)</td><td>536</td><td>1519</td></tr><tr><td>Bunch and Hellemans (2004)</td><td>71</td><td>93</td><td>Benson (2012)</td><td>59</td><td>206</td></tr><tr><td>Ochoa and Corey (1997)</td><td>23</td><td>24</td><td>Gergaud et al (2016)</td><td>179</td><td>595</td></tr><tr><td>Lilley (1948)</td><td>28</td><td>33</td><td></td><td></td><td></td></tr></tbody></table>

Source: Nuvolari et al. (2021).
* Properties of the quality indicators:

* Three indicators correlated, but in limited way.

* Capture complementary dimensions of patent quality.

* WRI: captures incremental innovations.

* Two others: capture breakthrough innovations.
## Quality indicators for selected patents

<table><thead><tr><td>Patent no.</td><td>Year</td><td>Inventor</td><td>Invention</td><td>Woodcroft references</td><td>Patent eminence</td><td>Inventor eminence</td></tr></thead><tbody><tr><td>542</td><td>1733</td><td>John Kay</td><td>Flying shuttle</td><td>1</td><td>10</td><td>8</td></tr><tr><td>556</td><td>1736</td><td>Jonathan Hulls</td><td>Steam-propelled ship</td><td>9</td><td>0</td><td>5</td></tr><tr><td>931</td><td>1769</td><td>Richard Arkwright</td><td>Water frame</td><td>3</td><td>10</td><td>9</td></tr><tr><td>1111</td><td>1775</td><td>Richard Arkwright</td><td>Carding machine</td><td>15</td><td>3</td><td>9</td></tr></tbody></table>

Source: Nuvolari et al. (2021).
<figure><img src="image_230.png" /><figcaption>Average number of references per patents over time</figcaption></figure>

(a) Average number of references per patents in Woodcroft (1862), yearly and by decade.
<figure><img src="image_231.png" /><figcaption>Average number of references per patents over time</figcaption></figure>

(b) Average number of references per patents by decade.
* Building the Bibliographic Composite Index (BCI):

* Take into account time and sector correlated patterns.

* Regress each indicator on time and sectoral indicators, extracting residuals.

* Residuals capture share of variance due to intrinsic quality of patents.

* Extract latent common factor for each patent.
Empirical distribution of the BCI

<figure><img src="image_232.png" /></figure>

Source: Nuvolari et al. (2021).
Percentile plot of the BCI

<figure><img src="image_233.png" /></figure>

Source: Nuvolari et al. (2021).
* Hypothesis of Mokyr (1990):

* Macroinventions are generated by a serendipitous search process.

* Microinventions result of continuous and cumulative improvements, displaying temporal persistence.
* Hypothesis of Mokyr (1990):

* Macroinventions are generated by a serendipitous search process.

* Microinventions result of continuous and cumulative improvements, displaying temporal persistence.

* Results confirm the hypothesis:

* No time clustering of macroinventions (top 0.5% BCI).

* Strong time clustering of microinventions (bottom 99.5% BCI).
<figure><img src="image_234.png" /><figcaption>## Number of inventions per year</figcaption></figure>

Source: Nuvolari et al. (2021).
<figure><img src="image_235.png" /><figcaption>## Relative cumulative distribution of macro vs micro inventions</figcaption></figure>

### (a) All patents

Source: Nuvolari et al. (2021).
Relative cumulative distribution of macro vs micro inventions

<figure><img src="image_236.png" /><figcaption>(b) Textile sector</figcaption></figure>

Source: Nuvolari et al. (2021).
<figure><img src="image_237.png" /><figcaption>## Relative cumulative distribution of macro vs micro inventions</figcaption></figure>

### (c) Engine sector

Source: Nuvolari et al. (2021).
<figure><img src="image_238.png" /><figcaption>Relative cumulative distribution of macro vs micro inventions</figcaption></figure>

(d) Machines
## Examples of stated aims of invention for three macroinventions

<table><thead><tr><td>Patent no.</td><td>Year</td><td>Inventor</td><td>Invention</td><td>Excerpt from Woodcroft (1854)</td><td>Stated aims</td></tr></thead><tbody><tr><td>962</td><td>1770</td><td>James Hargreaves</td><td>Spinning jenny</td><td>"[...] making an engine [...] to be managed by one person only, which will spin, draw, and twist 16 or more threads at a time by a motion of one hand and a draw of the other"</td><td>Save labor; save time</td></tr><tr><td>1420</td><td>1784</td><td>Henry Cort</td><td>Puddling process</td><td>"[...] manufacturing iron and steel into bars [...] of purer quality, in larger quantity, by a more effectual application of fire and machinery, and with greater yield"</td><td>Save capital and raw materials; improve quality</td></tr><tr><td>1645</td><td>1788</td><td>Andrew Meikle</td><td>Threshing machine</td><td>"[...] the corn is thereby separated from the straw in less time, and in more effectual manner than by threshing or any other manner"</td><td>Save time; improve quality</td></tr></tbody></table>

Source: Nuvolari et al. (2021).
## Patentees' stated aims of invention, 1700–1799

<table><thead><tr><th>Stated aim</th><th>Top 0.5%</th><th>Top 1%</th><th>Top 2%</th><th>All patents</th></tr></thead><tbody><tr><td>Create employment</td><td>0</td><td>1.7</td><td>1.2</td><td>1.9</td></tr><tr><td>Improve working conditions</td><td>0</td><td>0</td><td>2.3</td><td>1.4</td></tr><tr><td>Save labor</td><td>2.9</td><td>3.4</td><td>4.7</td><td>4.2</td></tr><tr><td>Save time</td><td>11.8</td><td>8.5</td><td>7</td><td>5.2</td></tr><tr><td>Save capital and raw materials</td><td>8.8</td><td>5.1</td><td>7</td><td>30.8</td></tr><tr><td>Reduce consumer price</td><td>5.9</td><td>5.1</td><td>3.5</td><td>3.7</td></tr><tr><td>Improve quality</td><td>32.4</td><td>27.1</td><td>25.6</td><td>29.3</td></tr><tr><td>Import substitution</td><td>0</td><td>1.7</td><td>1.2</td><td>3.6</td></tr><tr><td>Government revenue</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Other government benefits</td><td>5.9</td><td>3.4</td><td>2.3</td><td>2.1</td></tr></tbody></table>

Source: Nuvolari et al. (2021).
Patents for inventions intended to save labor, 1700–1799

<table><thead><tr><th>Patent aim</th><th>Top 0.5%</th><th>Top 1%</th><th>Top 2%</th><th>All patents</th></tr></thead><tbody><tr><td>Labor-saving stated</td><td>2.9</td><td>3.4</td><td>4.7</td><td>3.9</td></tr><tr><td>Effectively labor-saving</td><td>38.2</td><td>38.9</td><td>38.4</td><td>15.3</td></tr></tbody></table>

Source: Nuvolari et al. (2021).
Technical change in cotton spinning
Maw et al. (2022)

* Textile and especially spinning: leading sector in IR.

* Early macro-inventions:

* Hargreaves' spinning jenny mid-1760s.

* Arkwright's water-frame late-1760s.

* Crompton's mule in late-1770s.

* Initial productivity gains from macro-inventions have been overstated.

* Larger gains made in the micro-inventions phase from the 1780s to 1830s.

* Explore nature of improvements made to early macro-inventions in cotton spinning.
<figure><img src="image_239.png" /><figcaption>Yarn added-value, 1788-1839</figcaption></figure>

Source: Harley (1998).
Contemporary estimates of machine spindleage in the UK cotton industry (spindles, 000)

<table><thead><tr><th></th><th>Jenny</th><th>Mule</th><th>Water-frame</th><th>Throstle</th><th>Total</th></tr></thead><tbody><tr><td>1784</td><td>283 (82%)</td><td>4 (1%)</td><td>60 (17%)</td><td>0</td><td>347</td></tr><tr><td>1787</td><td>1,605 (83%)</td><td>50 (3%)</td><td>286 (15%)</td><td>0</td><td>1,941</td></tr><tr><td>1789</td><td>1,400 (58%)</td><td>700 (29%)</td><td>310 (13%)</td><td>0</td><td>2,410</td></tr><tr><td>1811</td><td>156 (3%)</td><td>4,200 (90%)</td><td>311 (7%)</td><td></td><td>4,667</td></tr><tr><td>1845</td><td>0</td><td>13,000 (74%)</td><td>0</td><td>4,500 (26%)</td><td>17,500</td></tr></tbody></table>

Source: Maw et al. (2022).
* Use newspaper advertisements 1780–1835 for information on early cotton-spinning machinery.

* Locations: Manchester, Blackburn, Leeds, Glasgow, Belfast, Derby, Nottingham, Preston.

* Total of 1,465 advertisements.

* Most common: public auctions (63%).

* Contains type of machine, average size, location.
Example of advertisement for cotton-spinning machinery

b) Manchester Mercury, 8 Apr. 1788

<figure><img src="image_240.png" /></figure>
<figure><img src="image_241.png" /><figcaption>Cotton spinning machinery advertisements, 1780–1835</figcaption></figure>

Source: Maw et al. (2022).
Advertisements for UK cotton-spinning machinery, by location, 1780—1811

<figure><img src="image_242.png" /></figure>
Advertisements for UK cotton-spinning machinery, by location, 1812—1835

<figure><img src="image_243.png" /></figure>
<figure><img src="image_244.png" /><figcaption>Machinery advertisements by type of machine</figcaption></figure>

Source: Maw et al. (2022).
## Size of advertised spinning machines (spindles)

<figure><img src="image_245.png" /></figure>

Source: Maw et al. (2022).
## Size of advertised spinning machines (spindles)

<figure><img src="image_246.png" /><figcaption>b) Water-frames</figcaption></figure>

Source: Maw et al. (2022).
## Size of advertised spinning machines (spindles)

<figure><img src="image_247.png" /><figcaption>c) Throstles</figcaption></figure>

Source: Maw et al. (2022).
## Size of advertised spinning machines (spindles)

<figure><img src="image_248.png" /><figcaption>d) Mules</figcaption></figure>

Source: Maw et al. (2022).
## Productivity indicators in cotton spinning, c. 1760—c. 1835

<table><thead><tr><th></th><th>Spindles per machine</th><th>Spinners per machine</th><th>Spindles per spinner</th><th>Operatives per machine</th><th>Hanks spun per spindle, per day (and 'count')</th><th>Daily output per spinning machine (hanks)</th><th>Daily output per spinning machine (lbs)</th><th>Daily output per operative (hanks)</th><th>Change factor since 1760</th></tr></thead><tbody><tr><td>Hand spinning, 1760s</td><td>1</td><td>1</td><td>1</td><td>1</td><td>8 (16s)</td><td>8</td><td>0.5</td><td>8</td><td></td></tr><tr><td>Spinning jenny, 1770s</td><td>16</td><td>1</td><td>16</td><td>1</td><td>1.6 (16s)</td><td>25.6</td><td>1.6</td><td>25.6</td><td>3.2</td></tr><tr><td>Water-frame, 1770s</td><td>48</td><td>1</td><td>48</td><td>1.5</td><td>1.6 (24s)</td><td>76.8</td><td>3.2</td><td>51.2</td><td>6.4</td></tr><tr><td>Hand mule, 1770s</td><td>48</td><td>1</td><td>48</td><td>1.5</td><td>1.25 (40s)</td><td>60</td><td>1.5</td><td>40</td><td>5</td></tr><tr><td>Spinning jenny, 1790s</td><td>93</td><td>1</td><td>93</td><td>1.5</td><td>1.2 (16s)</td><td>111.6</td><td>7</td><td>74.4</td><td>9.3</td></tr><tr><td>Water-frame, 1790s</td><td>69</td><td>1</td><td>69</td><td>1.5</td><td>2 (24s)</td><td>138</td><td>5.8</td><td>92</td><td>11.5</td></tr><tr><td>Hand mule, 1790s</td><td>169</td><td>1</td><td>169</td><td>1.5</td><td>1.25 (40s)</td><td>211.25</td><td>5.3</td><td>140.8</td><td>17.6</td></tr><tr><td>Throttle, early 1830s</td><td>142</td><td>0.67</td><td>213</td><td>0.84</td><td>4.5 (24s)</td><td>639</td><td>26.6</td><td>760.7</td><td>95.1</td></tr><tr><td>Power-assisted mule, early 1830s</td><td>298</td><td>0.5</td><td>596</td><td>1.08</td><td>3 (40s)</td><td>894</td><td>22.4</td><td>827.8</td><td>103.5</td></tr></tbody></table>

Source: Maw et al. (2022).
Directed technical change
## Allen (2011)

* Why were macro-inventions invented in 18th-century Britain?

* Different input factor prices there and then:

* Wages higher in Britain than elsewhere (high-wage argument).

* Capital and energy prices lower in Britain than elsewhere (cheap-energy argument).

* Argument about supply and demand of innovation:

* Demand innovations that substitute capital and energy for labor, e.g., spinning jenny.

* Supply of practical skills (Mokyr's industrial enlightenment).
"The Industrial Revolution, in short, was invented in Britain in the XVIIIth century because it paid to invent it there, while it would not have been profitable to invent in other times and places."
Allen (2009)
# The high-wage argument

Building laborers' wages around the world, grams of silver per day

<figure><img src="image_249.png" /></figure>
# The high-wage argument

Subsistence ratio for building laborers: income relative to cost of subsistence basket

<figure><img src="image_250.png" /></figure>

Source: Allen (2011).
<figure><img src="image_251.png" /><figcaption>Wage rate relative to price of capital</figcaption></figure>

Source: Allen (2011). Rental price of capital: average price indices for iron, metals, wood, brick multiplied by interest rate plus depreciation rate.
<figure><img src="image_252.png" /><figcaption>Wage rate relative to price of energy</figcaption></figure>

Source: Allen (2011). Price of energy: price of a kilogram of fuel divided by energy content (in grams of silver per million BTUs (British Thermal Units).
* Why was Britain price and wage structure like this?

* Britain's success in the global economy: labor demand from international trade, despite growing population and urbanization.
* Why was Britain price and wage structure like this?

* Britain's success in the global economy: labor demand from international trade, despite growing population and urbanization.

* Geography: available coal deposits and cheap to extract.

* Raise ratio of price of labor to energy ⇒ demand for energy-using technology.

* Energy important input in production of metals and bricks (important component of price of capital) ⇒ fall in capital prices relative to wage ⇒ demand for capital-using technology.
## Price of energy, early 1700s

<figure><img src="image_253.png" /></figure>

Source: Allen (2011). Price of energy: price of a kilogram of fuel divided by energy content (in grams of silver per million BTUs (British Thermal Units).
* Why would high cost of labor induce labor-saving innovations?

* Innovators innovate if returns to innovation higher than money spent.

* Returns in terms of royalty: depends on adoption (need patent system).
* Why would high cost of labor induce labor-saving innovations?

* Innovators innovate if returns to innovation higher than money spent.

* Returns in terms of royalty: depends on adoption (need patent system).

* Standard isoquant model:

* Isoquant curve holds output constant at 1 unit showing combinations of two inputs.

* Firm has cost $C = rK + wN \Leftrightarrow K = C/r + (w/r)L$

* r: rental rate of capital K.

* w: wage rate of labor L.

* Cost combination of K and L to produce 1 unit.

* $w/r$ is wage relative to price of capital.
# The high-wage argument

<figure><img src="image_254.png" /></figure>

* $w/r$: slope of isocost (constant cost) lines.

* Steeper slope = high-wage firm.

* Intercept $C/r$ is unit cost of firm (intercept with K axis).

Source: Allen (2011).
# The high-wage argument

<figure><img src="image_255.png" /></figure>

* Equilibrium when isoquant and isocost are tangent.

* At H: combination of K and L for high-wage firm.

* At L: combination of K and L for low-wage firm.

Source: Allen (2011).
<figure><img src="image_256.png" /><figcaption>Biased technical change</figcaption></figure>

* New technology T with new combination of K and L to produce 1.

* At T, firm uses less K and L than at H and L.

* Adoption only if lower cost, which is true only for high-wage firm.

Source: Allen (2011).
<figure><img src="image_257.png" /><figcaption>Biased technical change</figcaption></figure>

* Technology in zone I: adoption by high-wage firm.

* Technology in zone III: adoption by low-wage firm.

* Technology in zone II: adoption by both firms.

Source: Allen (2011).
# The high wages argument

* High-wage and low-wage firms have opposite incentives to invente technology $T$.

* Techology in zone I might be invented in high-wage economy, but depends on cost-benefits from R&D investment.

* Proper patent system helps benefits.

* Size of market helps, with increasing returns to scale.
* High-wage and low-wage firms have opposite incentives to invente technology $T$.

* Technology in zone I might be invented in high-wage economy, but depends on cost-benefits from R&D investment.

* Proper patent system helps benefits.

* Size of market helps, with increasing returns to scale.

* Patterns of innovation consistent:

* Inventions in machines were capital and energy using (cheap-energy and capital argument).

* Inventions in textile sector were strongly labor-saving (high-wage argument).

* Micro-inventions helped broader adoption.
## Biased technical change

<figure><img src="image_258.png" /></figure>

Source: Allen (2011).
* Was Britain a high wage economy before industrialization?

* Allen highlights innovations in spinning, leading sector in IR:

"[I]t would not have paid to use spinning machines before the eighteenth century: hence, they were not invented earlier. The analysis of profitability turns on the history of women's wages relative to the cost of spinning machinery'." Allen (2009)

* Spinners' high wages prompted innovations in textile industry.
<figure><img src="image_259.png" /><figcaption>Earnings of a spinner relative to a building laborer</figcaption></figure>

Source: Allen (2015).
* Humphries and Scheinder (2018) "Spinning the Industrial Revolution" re-examine this thesis.

* Essential labor input: hand spinners.

* Necessary for Allen's argument: costs of hand spinning rose late 17th–early 18th.

* Measurement issues:

* Mostly women and children: seldom appear in statistics.

* Fragmented sources.

* Nature of remuneration: piece rates.

⇒ Earnings estimates require productivity estimates.
* Humphries and Scheinder (2018) criticize Allen's sources.

* Allen's productivity estimates rely on one source. Muldrew (2012)

* Muldrew (2012) based on biased observers (social commentators) and overestimate wages.
* Humphries and Scheinder (2018) criticize Allen's sources.

* Allen's productivity estimates rely on one source. Muldrew (2012)

* Muldrew (2012) based on biased observers (social commentators) and overestimate wages.

* Humphries and Scheinder (2018) improve measurement.

* New data sources: records of putting-out networks, records of poor laws, spinning schools, farms accounts, diaries.

* Suggests spinners did not have high or strongly growing wages.

* Exception: 1720s, but too early to explain innovations of 1760s.

⇒ “The motivation for mechanization must be sought elsewhere”
<figure><img src="image_260.png" /><figcaption>Nominal daily wages, decadal averages</figcaption></figure>

Source: Humphries and Schneider (2018).
<figure><img src="image_261.png" /><figcaption>Welfare ratios, decadal averages</figcaption></figure>

Source: Humphries and Schneider (2018).
<figure><img src="image_262.png" /><figcaption>Daily wages by source type, nominal daily</figcaption></figure>

Source: Humphries and Schneider (2018).
* Allen (2019) “Spinning their wheels” re-examine the criticism.
# The high wages argument

* Allen (2019) “Spinning their wheels” re-examine the criticism.

* Criticism of Humphries-Schneider data:

* Selection of spinning schools and poor laws data: individuals selected because of low productivity.

⇒ Bias toward low productivity and low wages of spinners.
# The high wages argument

* Allen (2019) “Spinning their wheels” re-examine the criticism.

* Criticism of Humphries-Schneider data:

* Selection of spinning schools and poor laws data: individuals selected because of low productivity.

⇒ Bias toward low productivity and low wages of spinners.

* Biases from putting-out network data: paid women were splitting money with other family members or other women hired by paid women + hours worked unknown.

⇒ This source might provide unreliable information.
# The high wages argument

* Allen (2019) “Spinning their wheels” re-examine the criticism.

* Criticism of Humphries-Schneider data:

* Selection of spinning schools and poor laws data: individuals selected because of low productivity.

⇒ Bias toward low productivity and low wages of spinners.

* Biases from putting-out network data: paid women were splitting money with other family members or other women hired by paid women + hours worked unknown.

⇒ This source might provide unreliable information.

* Allen's data comparable to other series produced with wider coverage, showing wage growth.
# The high wages argument

* Allen (2019) “Spinning their wheels” re-examine the criticism.

* Criticism of Humphries-Schneider data:

* Selection of spinning schools and poor laws data: individuals selected because of low productivity.

⇒ Bias toward low productivity and low wages of spinners.

* Biases from putting-out network data: paid women were splitting money with other family members or other women hired by paid women + hours worked unknown.

⇒ This source might provide unreliable information.

* Allen's data comparable to other series produced with wider coverage, showing wage growth.

⇒ “The data presented by Humphries and Schneider fail to sustain their critique of the ‘high wage’ explanation of the industrial revolution.”
<figure><img src="image_263.png" /><figcaption>Spinners' earnings (pence per day)</figcaption></figure>

Source: JHJW: Humphries and Weidsorf (2019); JHBS: Humphries and Schneider (2018); RCA: Allen (2015).
* Humphries and Schneider (2020) “Losing the thread” respond to the rebuttal to the criticism of the original thesis.
* Humphries and Schneider (2020) “Losing the thread” respond to the rebuttal to the criticism of the original thesis.

* Criticism of wage data used by Allen (low quality, small sample).

* Add 16 data sources, 1,700 observations less subject to biases. Find same results.

⇒ “Allen, rather than us, is spinning his wheels.”

⇒ “Allen has lost the thread of empirical evidence that connects economic historians to the subjects of their study.”
<figure><img src="image_264.png" /><figcaption>Piece rates by source type, nominal daily</figcaption></figure>

Source: Humphries and Schneider (2020).
<figure><img src="image_265.png" /><figcaption>Nominal piece rates from regression analysis</figcaption></figure>

Source: Humphries and Schneider (2020).
* Conversely, wages outside of Britain were perhaps not as low as claimed by Allen.

* Many evidence that wages relative to energy in France were high.

* E.g., Strasbourg wages. Geloso (2019)

* Allen: London wages >> Paris wages 1650–1786.

* Allen: gap with rest of France even bigger.

* Geloso: rest of France was not much poorer.

* Geloso: wages in France were not that low.
## Real wages of Paris workers as a share of real wages of London workers

<table><thead><tr><th></th><th>UNADJUSTED FIGURES (1)</th><th>STEPHENSON ADJUSTMENT (2)</th></tr></thead><tbody><tr><td>1650-1675</td><td>70.88%</td><td>88.60%</td></tr><tr><td>1675-1700</td><td>59.03%</td><td>73.78%</td></tr><tr><td>1700-1725</td><td>56.87%</td><td>71.08%</td></tr><tr><td>1725-1750</td><td>44.81%</td><td>56.01%</td></tr><tr><td>1750-1775</td><td>49.78%</td><td>62.22%</td></tr><tr><td>1775-1786</td><td>57.16%</td><td>71.45%</td></tr></tbody></table>

Source: Geloso (2019) based on Allen (2001) and Stephenson (2015).
# The high wages argument

<figure><img src="image_266.png" /><figcaption>Wages in Paris, Strasbourg and Cavaillon (grams of silver) 1702–1786</figcaption></figure>

Source: Gelso (2019).
## Wages in Alsace for workers per day by season (grams of silver)

<table><thead><tr><th rowspan="2">SEASON</th><th>OUVRIER</th><th>MOISONNEUR</th><th>FAUCHEUR</th><th>JOURNALIER</th><th>JOURNALIER</th><th>BATTEUR EN GRANGE</th><th>JOURNALIER</th><th>ALLEN'S DATA</th></tr><tr><th>SUMMER</th><th>SUMMER</th><th>SUMMER</th><th>SUMMER</th><th>WINTER</th><th>WINTER</th><th>ANNUAL</th><th>SUMMER</th></tr></thead><tbody><tr><td>1476-1500</td><td>4.95</td><td>4.89</td><td>4.64</td><td>3.96</td><td>3.51</td><td>2.80<sup>a</sup></td><td>3.74</td><td>3.96</td></tr><tr><td>1501-1525</td><td>4.50</td><td>3.60</td><td>5.22</td><td>3.74</td><td>2.52<sup>a</sup></td><td>2.55<sup>a</sup></td><td>3.13</td><td>3.67</td></tr><tr><td>1526-1550</td><td>4.05</td><td>5.08</td><td>4.95</td><td>5.09</td><td>3.24</td><td>3.33</td><td>4.17</td><td>3.82</td></tr><tr><td>1551-1575</td><td>5.63</td><td>4.46</td><td>3.82<sup>a</sup></td><td>4.59</td><td>2.61<sup>a</sup></td><td>2.39</td><td>3.60</td><td>3.47</td></tr><tr><td>1576-1600</td><td>5.54</td><td>4.29</td><td>3.67<sup>a</sup></td><td>3.87</td><td>2.51<sup>a</sup></td><td>2.54<sup>a</sup></td><td>3.19</td><td>3.40</td></tr><tr><td>1601-1625</td><td>6.17</td><td>4.19</td><td>3.85<sup>a</sup></td><td>4.37</td><td>2.63<sup>a</sup></td><td>2.48</td><td>3.50</td><td>4.49</td></tr><tr><td>1626-1650</td><td>9.32</td><td>8.15</td><td>6.98</td><td>8.46</td><td>4.77</td><td>4.82</td><td>6.62</td><td>4.78</td></tr><tr><td>1651-1675</td><td>8.78</td><td>6.30</td><td>5.63</td><td>5.31</td><td>3.51</td><td>3.65</td><td>4.41</td><td>-</td></tr><tr><td>1676-1700</td><td>6.06<sup>a</sup></td><td>5.27</td><td>4.54<sup>a</sup></td><td>5.27</td><td>3.10<sup>a</sup></td><td>3.29</td><td>4.19</td><td>-</td></tr><tr><td>1701-1725</td><td>6.91<sup>a</sup></td><td>5.99</td><td>5.17<sup>a</sup></td><td>6.29<sup>a</sup></td><td>3.53<sup>a</sup></td><td>3.60</td><td>4.90</td><td>2.88</td></tr><tr><td>1726-1750</td><td>4.30</td><td>4.83</td><td>3.6</td><td>5.01<sup>a</sup></td><td>3.06</td><td>3.51</td><td>4.03</td><td>2.88</td></tr><tr><td>1751-1775</td><td>4.50</td><td>5.09</td><td>4.36<sup>a</sup></td><td>4.50</td><td>3.60</td><td>3.51</td><td>4.05</td><td>3.31</td></tr></tbody></table>

aInterpolated years.

Source: Geloso (2019).
Institutions

Victor Gay

Visiting professor at University of Michigan

Toulouse School of Economics

Institute for Advanced Study in Toulouse

Winter 2024

<figure><img src="image_267.png" /></figure>

<figure><img src="image_268.png" /></figure>

<figure><img src="image_269.png" /></figure>

Toulouse
School of
Economics
What is the role of institutions in the emergence of modern economic growth?
* Defining institutions

* Constitutions and commitment

* Long-run empirical evidence

* State capacity
Defining 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)*
* 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.
* 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.

* Rational-theoretic approach:

* Importance of theory of selection of institutions.

* Selection through centralized (bargaining) or decentralized (evolutionary competition) process.
* Political institutions:

* Inclusive political institutions, e.g., parliaments with equal participation.

* Extractive political institutions, e.g., slavery and exploitation by the few.
* Economic institutions (requiring inclusive political institutions):

* Property rights.

* Functioning markets.

* Contractual opportunities.
* Economic institutions (requiring inclusive political institutions):

* 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.
Greif and Kingston (2011)

* Some disagree with rational-choice approach to institutions.

* Complementary view: institutions as equilibria.

* Emphasize theory of motivation and expectations.

* Institutions emerge as equilibrium in a game of multiple equilibria.

* Helps explain why “bad” institutions might persist.
Constitutions and commitment
* Historical context

* Constitutions and commitment

* Criticism
<figure><img src="image_270.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).
## Historical context: Industrial Revolution in Britain

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

<figure><img src="image_271.png" /></figure>

Source: Broadberry (2020) based on Broadberry, Guan and Li (2018).
Historical context: political revolution in Britain

* English Civil War (1642–1651).

* Conflict between monarchy (Charles I) and Parliamentarians over expropriations.

* Parliament prevailed, setting up Commonwealth (Cromwell).
* English Civil War (1642–1651).

* Conflict between monarchy (Charles I) and Parliamentarians over expropriations.

* Parliament prevailed, setting up Commonwealth (Cromwell).

* Instability ⇒ Charles II restored by Parliament 1660.

* 1680s: James II reestablish Crown supremacy over Parliament.

* Glorious Revolution of 1688.

* Parliament overthrows James II.

* Revolutionary Settlement and Declaration of Rights.
  ⇒ Parliamentary supremacy.
## North and Weingast (1989)

* Strong states and expropriation risk.

* First goal of a state: national defense, protection against invasion.

* Achieving this goal ⇒ strong state.

* But this implies risk of predation by the state.
## North and Weingast (1989)

* Strong states and expropriation risk.

* First goal of a state: national defense, protection against invasion.

* Achieving this goal ⇒ strong state.

* But this implies risk of predation by the state.

* High expropriation risk ⇒ Smaller expected returns to investment
  ⇒ Smaller incentives for effort, investment, innovation.

* Affects people, but also the state (through tax base).

⇒ States face trade-off between expropriation or not.
* Suppose trade-off in favor of no expropriation.

* Sovereign wants to establish property rights.

* Promise to enforce these today and in the future.
* Suppose trade-off in favor of no expropriation.

* Sovereign wants to establish property rights.

* Promise to enforce these today and in the future.

* But fundamental commitment problem.

* Subjects must believe enforce property rights in the future for reaping returns of current investments.

* But as returns grow, so do incentives to renege and expropriate.

* Private citizens understand this, so don't invest today.
* Suppose trade-off in favor of no expropriation.

* Sovereign wants to establish property rights.

* Promise to enforce these today and in the future.

* But fundamental commitment problem.

* Subjects must believe enforce property rights in the future for reaping returns of current investments.

* But as returns grow, so do incentives to renege and expropriate.

* Private citizens understand this, so don't invest today.

⇒ Sovereign must make credible commitment not to revise property rights.
* Two ways to solve commitment problem.
* Two ways to solve commitment problem.

* Reputation when repeated interactions.

* Reneging on earlier promises tarnishes reputation.

* Long-run costs decrease incentives for short-run expropriation.

* But requires valuation of future revenues (so little turnover).

* Unconstrained rulers cannot use reputation alone.
* Two ways to solve commitment problem.
* Two ways to solve commitment problem.

* Create credible commitment devices.

* Voluntarily restrict ability to expropriate in future.

* Constitutions (checks and balances) play this role today.

* Glorious revolution 1688 created such device.
* Before the Glorious Revolution: Crown expropriated wealth.

* Forced loans, not repaid on time or in full.

* Sale of monopolies in settled industries.

* Sale of public offices (reducing value of other offices).

* Seizure of property.
* Before the Glorious Revolution: Crown expropriated wealth.

* Forced loans, not repaid on time or in full.

* Sale of monopolies in settled industries.

* Sale of public offices (reducing value of other offices).

* Seizure of property.

* House of Commons composed by wealthy class (targets).
* Institutional changes after the Revolution:

* Legal disputes over property tried in common law courts.

* Statute of Monopolies: prohibition of creation of monopolies by sale of patents to existing businesses.

* Changes in rules of labor mobility and land tenure (enclosures).

⇒ Legislative and judiciary independent from monarchy.
* Why no tyranny of Parliament?

* Balance of power between Crown, Parliament, courts.

* Fractionalized elites with diverse interests (merchants vs landowners).
* Constitution and commitment ⇒ secure property rights

* Many benefits
* Constitution and commitment ⇒ secure property rights

* Many benefits

* ↑ borrowing capacity of government.

* Major advantage for warfare, leading to gains from trade.

* Development of private capital markets.

* Less rent-seeking activities by special-interest groups.

* Incentives for investment and innovation.

⇒ Inclusive institutions were crucial to secure property rights and spur modern economic growth.
<figure><img src="image_272.png" /><figcaption>Government Debt to GDP for Britain (1691–2015)</figcaption></figure>

Source: Steinsson (2020) based on Barro (1987) and Thomas and Dimsale (2017).
<figure><img src="image_273.png" /><figcaption>Interest Rates on Government Debt in England</figcaption></figure>

Source: Steinsson (2020) based on Clark (1996).
<figure><img src="image_274.png" /><figcaption>Log Tax revenues per capita for five European powers</figcaption></figure>

Source: Johnson and Koyama (2017) based on Karaman and Pamuk (2013).
Ogilvie and Carus (2014)

* No reason why wealth holders seek policies beneficial to the whole economy.

* Other key institutional features matter.
* Many examples of strong parliaments which created institutions not beneficial to economic growth.
* Many examples of strong parliaments which created institutions not beneficial to economic growth.

* Poland.

* Strong Parliament, controlled by large landowners.

* Enforced legal privilege and second serfdom (16th–19th centuries).

* Prevented policies to allocate resources efficiently and undertake productive investments.

⇒ Concentration of power in homogeneous parliamentary representation impede growth.
* Many examples of strong parliaments which created institutions not beneficial to economic growth.
* Many examples of strong parliaments which created institutions not beneficial to economic growth.

* German state of Württemberg.

* Strong Parliament with representatives from diverse commercial and industrial communities (16th–19th centuries).

* Granted legal monopolies to special-interest groups (guilds, merchant companies...)

* Prevented policies beneficial to whole economy, generating stagnation through monopolistic competition.

⇒ Promotion of particularized institutions rather than generalized institutions matter for growth.
* Many examples of strong parliaments which created institutions not beneficial to economic growth.
* Many examples of strong parliaments which created institutions not beneficial to economic growth.

* **Dutch Republic.**

* Strong Parliament with representatives of each Seven Provinces (16th–18th centuries).

* Business elites could secure rents at the expense of overall economy.

* After the miracle, stagnation post 1670.
# Strong parliaments and growth

* Many examples of strong parliaments which created institutions not beneficial to economic growth.

* **Dutch Republic.**

* Strong Parliament with representatives of each Seven Provinces (16th–18th centuries).

* Business elites could secure rents at the expense of overall economy.

* After the miracle, stagnation post 1670.

* Even in Britain: mercantilism, slavery, colonialism, protectionism (Corn Laws) created rents economies with ambiguous effects on growth.
# Strong parliaments and growth

* Many examples of strong parliaments which created institutions not beneficial to economic growth.

* **Dutch Republic.**

* Strong Parliament with representatives of each Seven Provinces (16th–18th centuries).

* Business elites could secure rents at the expense of overall economy.

* After the miracle, stagnation post 1670.

* Even in Britain: mercantilism, slavery, colonialism, protectionism (Corn Laws) created rents economies with ambiguous effects on growth.

⇒ Parliaments do not guarantee diversity of views and growth-enhancing policies.
Ogilvie and Carus (2014)

* Key distinction: generalized vs particularized institutions.

* Generalized institutions: rules apply uniformly to all. Good.

* Particularized institutions: rules apply by group membership (gender, religion, race, parentage, guilds). Bad.
Ogilvie and Carus (2014)

* Key distinction: generalized vs particularized institutions.

* Generalized institutions: rules apply uniformly to all. Good.

* Particularized institutions: rules apply by group membership (gender, religion, race, parentage, guilds). Bad.

* Need simultaneously two types of institutions:

* Guaranteeing private property.

* Enforcing contracts (enables resource allocation to best use).
Long-run empirical evidence
* Do institutions matter for long-run development?

* Extremely difficult question due to endogeneity.
* Do institutions matter for long-run development?

* Extremely difficult question due to endogeneity.

* Long-run empirical evidence from AJR's “Colonial origins”:

* Contemporaneous correlations. AJR (2005)

* Reversal of fortune. AJR (2002)

* Colonial origins. AJR (2001)

* Unbundling institutions. AJ (2005)
* (Previously) Main sources for measures of institutions:

* Polity IV.

* Freedom House.

* World Bank Governance Indicators.
* (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, 1789–2020.

* Consistent coding, extremely complete.

* Vastly superior to alternatives.
* 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.
* 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 (1973–2020).

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

• Word bank governance indicators (1996–2020). 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_275.png" /><figcaption>Log GDP per Capita and Property Rights, 2020</figcaption></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
<figure><img src="image_276.png" /><figcaption>Log GDP per Capita and Access to Justice, 2020</figcaption></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
<figure><img src="image_277.png" /><figcaption>Log GDP per Capita and Rule of Law, 2020</figcaption></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
Contemporaneous correlations

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

<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>2020</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 such as geography or culture.
* 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 such as geography or culture.

* General solution: find exogenous source of variation.
<figure><img src="image_278.png" /><figcaption>Log GDP per Capita and Latitude, 2020</figcaption></figure>

Source: Maddison Project Database 2018, CEPII GeoDist, 172 countries.
<figure><img src="image_279.png" /><figcaption>Rule of Law and Latitude, 2020</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_280.png" /><figcaption>Rule of Law in the Koreas</figcaption></figure>

Source: V-Dem 9.
<figure><img src="image_281.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 natural experiment to assess the causal impact of institutions on economic development.
# Reversal of fortune

* Acemoglu, Johnson and Robinson (2002)

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

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

* Use colonization as a natural experiment to assess the causal impact of institutions on 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 (measurement issues).

* Sample sizes small when paper written.
## Urbanization in 1500 and Log GDP per Capita in 1995
Former European Colonies

<figure><img src="image_282.png" /></figure>

Source: Penn World Table 9, CEPII GeoDist. AJR (2002), 40 countries.
# Reversal of fortune

<figure><img src="image_283.png" /><figcaption>FIGURE I</figcaption></figure>

Log GDP per Capita (PPP) in 1995 against Urbanization Rate in 1500

*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.
# 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

<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.
# Reversal of fortune: non-colonies

<figure><img src="image_284.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_285.png" /></figure>

Source: AJR (2002), 30 countries.
## Evolution of Urbanization in Former European Colonies

<figure><img src="image_286.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 ($\alpha_1$) $\Rightarrow$ prosperity.

$$Y_{it} = \alpha_0 + \alpha_1 \cdot G_i + \nu_t + \varepsilon_{it}$$

* 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_287.png" /><figcaption>Urbanization in 1500 and Property Rights in 2020
Former European Colonies</figcaption></figure>

Source: V-Dem 9, AJR (2002), 40 countries.
## Urbanization in 1500 and Access to Justice in 2020
Former European Colonies

<figure><img src="image_288.png" /></figure>

Source: V-Dem 9, AJR (2002), 40 countries.
## Urbanization in 1500 and Rule of Law in 2020
Former European Colonies

<figure><img src="image_289.png" /></figure>

Source: V-Dem 9, AJR (2002), 40 countries.
* Evidence of institutional reversal.

* High urbanization in 1500 ⇒ “extractive institutions.”

* Low urbanization in 1500 ⇒ “inclusive 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 their property.

* 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.

Settler mortality → Settlements → Early institutions

Current development ← Current institutions ←
Log GDP per Capita and Rule of Law, 2020. Causal?

<figure><img src="image_290.png" /></figure>

Source: V-Dem 9, Maddison Project Database 2018, 160 countries.
<figure><img src="image_291.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, 2020
Former European Colonies

<figure><img src="image_292.png" /></figure>

Source: V-Dem 9, AJR (2001), 62 countries.
<figure><img src="image_293.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 2020</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_294.png" /><figcaption>Settler Mortality and Log GDP per Capita, 2020
Former European Colonies</figcaption></figure>

Source: Maddison Project Database 2018, AJR (2001), 62 countries.
<figure><img src="image_295.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 2020</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.
Colonial origins: IV regressions

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Log GDP per Capita in 2020</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Rule of Law in 2020</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>

<footnote>*** Significant at the 1 percent level. * Significant at the 1 percent level.</footnote>
* 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)
* 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
* 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_296.png" /><figcaption>Log GDP per Capita and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 108 countries.
<figure><img src="image_297.png" /><figcaption>Log GDP per Capita and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 121 countries.
<figure><img src="image_298.png" /><figcaption>Log GDP per Capita and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 122 countries.
<figure><img src="image_299.png" /><figcaption>Log GDP per Capita and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 145 countries.
<figure><img src="image_300.png" /><figcaption>Log GDP per Capita and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 110 countries.
<figure><img src="image_301.png" /><figcaption>Log GDP per Capita and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 129 countries.
<figure><img src="image_302.png" /><figcaption>Investment to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 110 countries.
<figure><img src="image_303.png" /><figcaption>Investment to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 124 countries.
<figure><img src="image_304.png" /><figcaption>Investment to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 125 countries.
<figure><img src="image_305.png" /><figcaption>Investment to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 149 countries.
<figure><img src="image_306.png" /><figcaption>Investment to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 113 countries.
<figure><img src="image_307.png" /><figcaption>Investment to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 131 countries.
<figure><img src="image_308.png" /><figcaption>Credit to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 103 countries.
<figure><img src="image_309.png" /><figcaption>Credit to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 117 countries.
<figure><img src="image_310.png" /><figcaption>Credit to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 118 countries.
<figure><img src="image_311.png" /><figcaption>Credit to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 140 countries.
<figure><img src="image_312.png" /><figcaption>Credit to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 109 countries.
<figure><img src="image_313.png" /><figcaption>Credit to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 126 countries.
<figure><img src="image_314.png" /><figcaption>Capitalization to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 89 countries.
<figure><img src="image_315.png" /><figcaption>Capitalization to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 91 countries.
<figure><img src="image_316.png" /><figcaption>Capitalization to GDP and Contracting, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 91 countries.
<figure><img src="image_317.png" /><figcaption>Capitalization to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 105 countries.
<figure><img src="image_318.png" /><figcaption>Capitalization to GDP and Property Rights, 1990s
All Countries</figcaption></figure>

Source: AJ (2005), 95 countries.
<figure><img src="image_319.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: $\text{Cov}(\varepsilon_c, L_c) = \text{Cov}(\varepsilon_c, I_c) = 0$.

* $\varepsilon_c$: 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.
<figure><img src="image_320.png" /><figcaption>Partial Correlations</figcaption></figure>

Source: AJ (2005), 54 countries.

<figure><img src="image_321.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.
<figure><img src="image_322.png" /><figcaption>Partial Correlations</figcaption></figure>

Source: AJ (2005), 72 countries.

<figure><img src="image_323.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.

* 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 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).
* What enables a society to adopt growth enhancing institutions?
* What enables a society to adopt growth enhancing institutions?

* Concept of state capacity:

* 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)
* What enables a society to adopt growth enhancing institutions?

* Concept of state capacity:

* 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_324.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_325.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_326.png" /></figure>
## Judicial Institutions in France (Bailliages)

<figure><img src="image_327.png" /></figure>
<figure><img src="image_328.png" /><figcaption>## Judicial Institutions in France (Bailliages)</figcaption></figure>
<figure><img src="image_329.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_330.png" /><figcaption>## Per capita revenues, Prussia</figcaption></figure>

Source: Dincecco (2009)
<figure><img src="image_331.png" /><figcaption>Per capita revenues, Austria-Hungary</figcaption></figure>

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

<figure><img src="image_332.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.
<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>
<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.
Time-demeaned per capita GDP growth rates and fiscal centralization

<figure><img src="image_333.png" /></figure>
Time-demeaned per capita GDP growth rates and limited government

<figure><img src="image_334.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:

$$ \Delta y_{i,t} = \alpha_0 + \sum_{j=1}^{5} \alpha_{1,j} \tilde{C}_{i,t}^j + \sum_{j=1}^{5} \alpha_{1,j} \tilde{L}_{i,t}^j + X_{i,t-1}^{\prime} \alpha_3 + \mu_i + \lambda_t + \varepsilon_{i,t} $$

* $\tilde{C}_{i,t}^1, \tilde{L}_{i,t}^1 = 1$ 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_335.png" /></figure>
Time-varying relationship between gdp per capita growth and limited government

<figure><img src="image_336.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_337.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.
# 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 warfare a necessary condition for the creation of Parliaments in Europe?
* 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 warfare a necessary condition for the creation of Parliaments in Europe?

* Is this logic universal?
Cox, Dincecco, and Onorrato (2024)

* Is warfare a necessary condition for the creation of Parliaments in Europe?
Cox, Dincecco, and Onorrato (2024)

* Is warfare a necessary condition for the creation of Parliaments in Europe?

* Some very specific conditions in Europe:

* After collapse of Roman Empire, no central bureaucracies to collect taxes.

* During the medieval communal revolution (1000–1300), many European towns acquired self-governance.

* To collect taxes to finance wars, rulers had to use these self-governing towns into a decentralized tax administration system.

* Parliaments emerged earlier in polities whose major towns became a commune earlier.
The communal revolution and parliamentary births in Medieval Europe

<figure><img src="image_338.png" /></figure>

Source: Cox, Dincecco, and Onorrato (2024)
* Data:

* 44 polities: sovereign non-tribal entity in Crhistendom in 1200; 5,000+ km2 in 1200; survived 100+ years.

* Parliamentary birth year: first year in which tax-controlling council included non-noble urban elites (power to reject or approve extraordinary taxatino by majority vote).

* Warfare: 848 unique conflicts over 1000–1599 from Jacques (2007)
* Empirical strategy:

$$ \text{Parliament Birth}_{it} = \beta \text{ Conflict}_{i(t-p,t-1)} + \alpha_i + \lambda_t + X'_{it}\Phi + \varepsilon_{it} $$

* $\text{Conflict}_{i(t-p,t-1)}$: average number of military conflicts per year for polity i over previous years p (benchmark: p = 25 years).

* $\alpha_i$: polity FE controlling for initial economic, cultural, institutional conditions. $\lambda_t$: year FE.

* $X'_{it}$: time-varying controls: trade potential, years of ruler successions, lack of male heirs at time of succession, primogeniture.

* Standard errors: clustered at polity level.

* Restrict sample to polity-years in century after first commune established in polity.
War and parliamentary births

<table><caption>**Table 2.** War and parliamentary births</caption><thead><tr><th>Dependent variable</th><th>1</th><th>2<br>Parliamentary birth</th><th>3</th></tr></thead><tbody><tr><td>Conflict participation</td><td>0.055***<br>(0.016)</td><td>0.055***<br>(0.018)</td><td>0.056***<br>(0.018)</td></tr><tr><td>Trade potential</td><td></td><td></td><td>-0.033<br>(0.031)</td></tr><tr><td>Polity FE</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Year FE</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Polity trends</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Years included</td><td>Post-commune</td><td></td><td></td></tr><tr><td># Observations</td><td>8,725</td><td>8,725</td><td>8,725</td></tr><tr><td># Polities</td><td>37</td><td>37</td><td>37</td></tr><tr><td>R<sup>2</sup> (within)</td><td>0.134</td><td>0.146</td><td>0.146</td></tr></tbody></table>

*Notes:* The estimation method is OLS. The unit of analysis is polity-year. The sample period is 1000–1599. Robust standard errors clustered at polity level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10.

Source: Cox, Dincecco, and Onorrato (2024)

* 1 conflict per year average over past 25 years $\Rightarrow$ 5.6 pp higher per-year probability of parliamentary birth.
* 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 warfare a necessary condition for the creation of Parliaments in Europe?
* 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 warfare a necessary condition for the creation of Parliaments in Europe?

* Is this logic universal?
<figure><img src="image_339.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.
* 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.
* Political geography: a mediator between warfare and political development.
* 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_340.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: / ≫ 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 ⇌ 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.

• $l \gg 0$ ⇐⇒ ruler grants rather than deny ($l - p > 0$).