# Main Results

<table><caption>Table 3 State history, log population density and urbanization in 1500 CE</caption><thead><tr><th rowspan="2">Panel A</th><th colspan="7">Log population density in 1500 CE</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Statehist in 1500 CE</td><td>3.680***<br>(0.566)</td><td>8.730***<br>(1.557)</td><td></td><td>6.262***<br>(1.902)</td><td>6.496***<br>(1.557)</td><td>4.829***<br>(1.514)</td><td>7.689***<br>(2.433)</td></tr><tr><td>Statehist in 1500 CE squared</td><td></td><td>-10.385***<br>(2.663)</td><td></td><td>-8.856***<br>(2.855)</td><td>-5.635**<br>(2.702)</td><td>-2.832<br>(2.139)</td><td>-3.447<br>(2.131)</td></tr><tr><td>Agyears in 1500 CE</td><td></td><td></td><td>0.315***<br>(0.042)</td><td>0.186***<br>(0.065)</td><td>0.159***<br>(0.060)</td><td>0.139**<br>(0.065)</td><td>0.230***<br>(0.068)</td></tr><tr><td>Origtime in 1500 CE</td><td></td><td></td><td></td><td></td><td>0.005**<br>(0.003)</td><td>-0.003<br>(0.004)</td><td>-0.021<br>(0.014)</td></tr><tr><td>Origtime in 1500 CE squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.000<br>(0.000)</td></tr><tr><td>State age in 1500 CE</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.406**<br>(0.188)</td></tr><tr><td>Observations</td><td>154</td><td>154</td><td>147</td><td>147</td><td>128</td><td>128</td><td>128</td></tr><tr><td>R-squared</td><td>0.214</td><td>0.269</td><td>0.269</td><td>0.321</td><td>0.716</td><td>0.770</td><td>0.787</td></tr></tbody></table>
# Main Results

<table><caption>Table 3 continued</caption><thead><tr><th>Panel B</th><th colspan="7">Urbanization in 1500 CE</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Statehist in 1500 CE</td><td>15.392***<br>(2.183)</td><td>25.808***<br>(7.147)</td><td></td><td>30.826***<br>(8.168)</td><td>41.316***<br>(10.318)</td><td>35.336***<br>(10.870)</td><td>50.123***<br>(16.171)</td></tr><tr><td>Statehist in 1500 CE squared</td><td></td><td>-20.101*<br>(11.798)</td><td></td><td>-23.144*<br>(11.777)</td><td>-32.852**<br>(12.374)</td><td>-25.467*<br>(12.882)</td><td>-28.826**<br>(14.072)</td></tr><tr><td>Ageyears in 1500 CE</td><td></td><td></td><td>0.761***<br>(0.177)</td><td>-0.391<br>(0.248)</td><td>-0.175<br>(0.307)</td><td>-0.245<br>(0.406)</td><td>0.245<br>(0.483)</td></tr><tr><td>Origtime in 1500 CE</td><td></td><td></td><td></td><td></td><td>-0.081**<br>(0.037)</td><td>-0.084*<br>(0.042)</td><td>-0.213<br>(0.145)</td></tr><tr><td>Origtime in 1500 CE squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.002<br>(0.002)</td></tr><tr><td>State age in 1500 CE</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-2.067<br>(1.503)</td></tr><tr><td>Observations</td><td>83</td><td>83</td><td>83</td><td>83</td><td>76</td><td>76</td><td>76</td></tr><tr><td>R-squared</td><td>0.302</td><td>0.323</td><td>0.111</td><td>0.337</td><td>0.480</td><td>0.507</td><td>0.534</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr></tbody></table>
# Main Results

<table><caption>Table 4 State history and average technology adoption 2000 CE</caption><thead><tr><th rowspan="2">Panel A</th><th colspan="7">Technology adoption in 2000 CE</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Statehist</td><td>0.086<br>(0.095)</td><td>0.842***<br>(0.318)</td><td></td><td>0.667**<br>(0.331)</td><td>0.303<br>(0.239)</td><td>0.461*<br>(0.243)</td><td>0.674**<br>(0.282)</td></tr><tr><td>Statehist squared</td><td></td><td>-1.285***<br>(0.452)</td><td></td><td>-1.193**<br>(0.459)</td><td>-0.405<br>(0.347)</td><td>-0.553*<br>(0.323)</td><td>-0.574*<br>(0.316)</td></tr><tr><td>Agyears</td><td></td><td></td><td>0.011<br>(0.007)</td><td>0.011<br>(0.010)</td><td>-0.007<br>(0.008)</td><td>-0.005<br>(0.012)</td><td>0.003<br>(0.012)</td></tr><tr><td>Origtime</td><td></td><td></td><td></td><td>0.000<br>(0.000)</td><td>0.001**<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.002<br>(0.002)</td></tr><tr><td>Origtime squared</td><td></td><td></td><td></td><td></td><td></td><td>0.000<br>(0.000)</td><td>0.000<br>(0.000)</td></tr><tr><td>State age</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.036*<br>(0.021)</td></tr><tr><td>Observations</td><td>130</td><td>130</td><td>129</td><td>129</td><td>125</td><td>125</td><td>125</td></tr><tr><td>R-squared</td><td>0.006</td><td>0.044</td><td>0.016</td><td>0.050</td><td>0.643</td><td>0.683</td><td>0.690</td></tr></tbody></table>
<table><caption>Table 4 continued</caption><thead><tr><th rowspan="2">Panel B</th><th colspan="7">Technology adoption in 2000 CE</th></tr><tr><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>Ancestry-Adjusted Stateist of 1500 CE</td><td>0.219**<br>(0.093)</td><td>1.285***<br>(0.273)</td><td></td><td>1.271***<br>(0.310)</td><td>0.785***<br>(0.216)</td><td>0.661***<br>(0.220)</td><td>0.791***<br>(0.221)</td></tr><tr><td>Ancestry-Adjusted Stateist of 1500 CE squared</td><td></td><td>-2.024***<br>(0.505)</td><td></td><td>-2.021***<br>(0.510)</td><td>-1.120***<br>(0.289)</td><td>-0.930***<br>(0.320)</td><td>-0.892***<br>(0.304)</td></tr><tr><td>Agyears</td><td></td><td></td><td>0.011<br>(0.007)</td><td>0.001<br>(0.011)</td><td>-0.010<br>(0.009)</td><td>-0.005<br>(0.011)</td><td>0.002<br>(0.012)</td></tr><tr><td>Origtime</td><td></td><td></td><td></td><td></td><td>0.001*<br>(0.000)</td><td>0.001**<br>(0.001)</td><td>0.001<br>(0.002)</td></tr><tr><td>Origtime squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.000<br>(0.000)</td></tr><tr><td>State age</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.030*<br>(0.017)</td></tr><tr><td>Observations</td><td>130</td><td>130</td><td>129</td><td>129</td><td>125</td><td>125</td><td>125</td></tr><tr><td>R-squared</td><td>0.033</td><td>0.140</td><td>0.016</td><td>0.138</td><td>0.674</td><td>0.698</td><td>0.704</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr></tbody></table>
# Main Results

<table><caption>Table 5 Statehist versus Statehist 1–1950 CE and Log GDP pc 2000. Nonlinear relationship</caption><thead><tr><th rowspan="2">Panel A</th><th colspan="7">Log GDP pc 2000</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Statehist</td><td>1.326*<br>(0.723)</td><td>7.010***<br>(2.291)</td><td></td><td>7.337***<br>(2.658)</td><td>3.869**<br>(1.921)</td><td>4.530**<br>(2.057)</td><td>6.790***<br>(2.496)</td></tr><tr><td>Statehist squared</td><td></td><td>-9.842***<br>(3.529)</td><td></td><td>-9.832***<br>(3.549)</td><td>-4.718<br>(2.854)</td><td>-4.970*<br>(2.793)</td><td>-4.657*<br>(2.776)</td></tr><tr><td>Agyears</td><td></td><td></td><td>0.105**<br>(0.048)</td><td>0.004<br>(0.079)</td><td>-0.071<br>(0.063)</td><td>-0.087<br>(0.079)</td><td>0.010<br>(0.081)</td></tr><tr><td>Origtime</td><td></td><td></td><td></td><td></td><td>0.002<br>(0.003)</td><td>0.008**<br>(0.004)</td><td>0.010<br>(0.013)</td></tr><tr><td>Origtime squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.000)</td></tr><tr><td>State age</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.460**<br>(0.183)</td></tr><tr><td>Observations</td><td>154</td><td>154</td><td>147</td><td>147</td><td>125</td><td>125</td><td>125</td></tr><tr><td>R-squared</td><td>0.020</td><td>0.052</td><td>0.026</td><td>0.064</td><td>0.702</td><td>0.719</td><td>0.734</td></tr></tbody></table>
# Main Results

<table><caption>Table 5 continued</caption><thead><tr><th rowspan="2">Panel B</th><th colspan="7">Log GDP pc 2000</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Statehist 1–1950 CE</td><td>1.277**<br>(0.531)</td><td>1.940<br>(2.049)</td><td></td><td>2.200<br>(2.278)</td><td>0.066<br>(1.441)</td><td>0.251<br>(1.597)</td><td>1.267<br>(1.667)</td></tr><tr><td>Statehist 1–1950 CE squared</td><td></td><td>−0.783<br>(2.518)</td><td></td><td>−0.748<br>(2.625)</td><td>0.942<br>(1.608)</td><td>0.962<br>(1.811)</td><td>0.453<br>(1.776)</td></tr><tr><td>Agyears</td><td></td><td></td><td>0.105**<br>(0.048)</td><td>−0.011<br>(0.068)</td><td>−0.069<br>(0.055)</td><td>−0.080<br>(0.072)</td><td>0.012<br>(0.081)</td></tr><tr><td>Origime</td><td></td><td></td><td></td><td></td><td>0.001<br>(0.003)</td><td>0.007*<br>(0.004)</td><td>0.011<br>(0.013)</td></tr><tr><td>Origime squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>−0.000<br>(0.000)</td></tr><tr><td>State age</td><td></td><td></td><td></td><td></td><td></td><td></td><td>−0.267**<br>(0.127)</td></tr><tr><td>Observations</td><td>154</td><td>154</td><td>147</td><td>147</td><td>125</td><td>125</td><td>125</td></tr><tr><td>R-squared</td><td>0.043</td><td>0.044</td><td>0.026</td><td>0.058</td><td>0.704</td><td>0.722</td><td>0.730</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr></tbody></table>
# Main Results

<table><caption>Table 6 Adjusted Statehist and Log GDP pc 2000. Nonlinear relationship</caption><thead><tr><th rowspan="2"></th><th colspan="7">Log GDP pc 2000</th></tr><tr><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>Ancestry-Adjusted Statehist of 1500 CE</td><td>2.934***<br>(0.784)</td><td>12.888***<br>(2.183)</td><td></td><td>12.991***<br>(2.092)</td><td>6.016***<br>(1.572)</td><td>5.292***<br>(1.644)</td><td>6.033***<br>(1.774)</td></tr><tr><td>Ancestry-Adjusted Statehist of 1500 CE squared</td><td></td><td>-19.143***<br>(4.322)</td><td></td><td>-18.453***<br>(4.029)</td><td>-8.971***<br>(2.201)</td><td>-7.495***<br>(2.349)</td><td>-6.794***<br>(2.332)</td></tr><tr><td>Ageyears</td><td></td><td></td><td>0.105**<br>(0.048)</td><td>-0.026<br>(0.059)</td><td>-0.054<br>(0.055)</td><td>-0.073<br>(0.078)</td><td>-0.023<br>(0.082)</td></tr><tr><td>Origtime</td><td></td><td></td><td></td><td></td><td>0.003<br>(0.003)</td><td>0.006*<br>(0.004)</td><td>0.007<br>(0.013)</td></tr><tr><td>Origtime squared</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.000)</td></tr><tr><td>State age</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.239<br>(0.147)</td></tr><tr><td>Observations</td><td>148</td><td>148</td><td>147</td><td>144</td><td>125</td><td>125</td><td>125</td></tr><tr><td>R-squared</td><td>0.093</td><td>0.234</td><td>0.026</td><td>0.238</td><td>0.722</td><td>0.727</td><td>0.732</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr></tbody></table>
Main Results

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

Extended Statehist 3500 BCE - 2000 CE (1% discount)
* Interpretation:

* Indonesia: 1350 years of state existence (score of 0.254).

* Increasing the score by 0.1 (59% of 1 sd, reaching UK) ⇒ +20% GDP per capita in 2000.

* Inverse for China (score of 0.582): -44% GDP per capita in 2000.
J Econ Growth (2014) 19:439–470
DOI 10.1007/s10887-014-9108-6

Asia’s little divergence: state capacity in China and Japan
before 1850

Tuan-Hwee Sng · Chiaki Moriguchi
* Sng and Moriguchi (2014)

* Why was Japan the first non-Western country to industrialize?

* Why did China take longer to modernize?

* Explore the role of state capacity for development in China and Japan.
<figure><img src="image_170.png" /><figcaption>Fig. 1 Per Capita Tax Revenue in China and Japan. Sources: Shogunate's land tax from Ohno (1996); Japan's population estimates from Hayami and Miyamoto (1988); China's tax revenues from Sng (2014); China's population estimates from Perkins (1969)</figcaption></figure>

Low state capacity in China: absence of warfare and political geography?
Why Japan so high?
<figure><img src="image_171.png" /><figcaption>Fig. 2 Early modern China and Japan. Source: CHGIS, Version 4, Cambridge: Harvard Yenching Institute, January 2007</figcaption></figure>
<figure><img src="image_172.png" /><figcaption>Fig. 3 Tokugawa Japan in 1664. Source: China Historical GIS Project, "Tokugawa Japan GIS, Demo Version." Feb 2004</figcaption></figure>
* Stable dictatorships 1650–1850 in both countries.

* But one dictator in China (Qing dynasty) and multiple in Japan.

* Tokugawa Japan: 1 shogunate and league of dictatorships (each of 260 daimyo is a dictator).

* Shogunate: centralized bureaucracy with 2 layers: center, local. 40–50 jurisdictions.

* Qhing China: centralized bureaucracy with 4 layers: center, province, prefecture, local. 1,500 jurisdictions.
* China: 90 times bigger than shogunate, so high monitoring costs.

* Similar monitoring systems:

* Top-down monitoring: local officials supervised by higher-ranking officials within same bureaucratic hierarchy. Ineffective if bureaucratic patronage networks.

* Bottom-up monitoring: petition systems. But extremely costly, especially in China.
* Land taxation: most important source of revenue.

* Every land holding household pay land tax depending on size and quality of land.

* In Japan: fiscal base in rice (staple crop nationwide) collected on village as units.

* In China: base depends on region due to variety of crops and metals in China collected on households as units.

* Hence, more contacts between tax officials and individuals in China, increasing potential for tax officials to abuse power + monitoring delegated to village communities in Japan.
* Model relation between geography and ruler's capacity to collect taxes and provide public goods.

* Prediction 1: lower legal tax rates and higher extralegal expropriation rates in China than Japan due to difficulties in monitoring tax agents in China.

* Prediction 2: when agency problems are more severe, rule has less incentives to invest in public goods in the region because returns to taxation are lower.

* Prediction 3: economic expansions are less beneficial to dictators in large dictatorships due to greater agency costs.

* Summary: lower corruption, higher tax rates, and higher production of public goods in Japan than China due to agency costs.
* Corruption: higher in China than Japan.

* China: over-collection through manipulation of commutation rates above market rate (pay in copper instead of grain). Estimates of 20% of China's agricultural output in 1850 based on magistrates' incomes.

* Japan: use prosecution cases for corruption, with estimates of bribes as share of output of maximum 13%.
* Corruption: higher in China than Japan.

* China: over-collection through manipulation of commutation rates above market rate (pay in copper instead of grain). Estimates of 20% of China's agricultural output in 1850 based on magistrates' incomes.

* Japan: use prosecution cases for corruption, with estimates of bribes as share of output of maximum 13%.

* Tax rate: higher in Japan than in China.
* Corruption: higher in China than Japan.

* China: over-collection through manipulation of commutation rates above market rate (pay in copper instead of grain). Estimates of 20% of China's agricultural output in 1850 based on magistrates' incomes.

* Japan: use prosecution cases for corruption, with estimates of bribes as share of output of maximum 13%.

* Tax rate: higher in Japan than in China.

* Public goods: higher in Japan than China (coinage, roads, famine relief, forest protection).
Data: Tax Rates

<figure><img src="image_173.png" /><figcaption>Fig. 1 Per Capita Tax Revenue in China and Japan. Sources: Shogunate's land tax from Ohno (1996); Japan's population estimates from Hayami and Miyamoto (1988); China's tax revenues from Sng (2014); China's population estimates from Perkins (1969)</figcaption></figure>
<table><caption>**Table 1** Public goods provision in Qing China and Tokugawa Japan</caption><thead><tr><th></th><th>China</th><th>Japan</th></tr></thead><tbody><tr><td>(a) Coinage</td><td>Copper only</td><td>Gold, Silver, Copper</td></tr><tr><td>Annual output of copper coins, aggregate</td><td>3,639,800 k (1756–65)</td><td>1,096,000 k (1764–88)</td></tr><tr><td>Annual output of copper coins, per capita</td><td>15 (1756–1765)</td><td>35 (1764–1788)</td></tr><tr><td>(b) Length of trunk roads (km)</td><td>11,370 (imperial routes)</td><td>1,440 (Gokaido)</td></tr><tr><td>Length per '00 sq. km</td><td>0.26</td><td>0.51 or 3.37</td></tr><tr><td>(c) Urban population (urbanization rate)</td><td>20.5 m (5.8 %)</td><td>5.1 m (16.5 %)</td></tr><tr><td>(d) Forest cover (million ha)</td><td>18.5 (1700) → 9.6 (1850)<br>(Lingnan region only)</td><td>27 (1600) → 25.5 (1850)</td></tr><tr><td>(e) Grain stockpile per capita (husked rice, koku)</td><td>0.065 (1751)<br>→ 0.030 (1843)</td><td>0.038 (1751)<br>→ 0.046 (1843)</td></tr></tbody></table>

Sources: (1) Lin (2006) and Tsuchiya and Yamaguchi (1972); (2) DQHD (Yongzheng edition) and Vaporis (1994); (3) Rozman (1973, Table 5); (4) Saito (2009); (5) Eto (1970); Yoshida (1991); Wang (1985)[1890]; Will and Wong (1991); Li and Jiang (2008)
How path dependent is the relationship between State capacity and growth?
* This course explores the interactions between institutions and alternative explanations (culture, geography).

* One additional explanation is path dependence.

* Here, non-linear relationship:

* Statehood persists over time.

* But non-linear effects on growth due to unlimited constraints to state power and elite capture.

* Might help explain the Little Divergence and the downfall of China.
Topic 4

Geography, institutions, and development
Can geography alone explain differences in long-run
economic development?
* Long-term effects of geography

* The legacy of the Neolithic transition

* Reversal of fortune

* The disease environment

* The fractured land hypothesis
## Spolaore and Wacziarg (2013)

* Long-standing geography hypothesis (starting with Montesquieu):

* Climate, temperature, ruggedness.

* Disease environment.

* Natural resources.

⇒ Direct effects on productivity and development?
* Prehistoric and biological conditions ⇒ spread of agriculture and domestication. Diamond (1997) Olsson and Hibbs (2005)

* Initial advantages of Eurasia:

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

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

* Lead from transition enabled Europeans to dominate other regions.

* Empirical evidence for correlation between biogeographic endowments and long-run development.
# Long-term effects of geography

## GEOGRAPHY AND CONTEMPORARY DEVELOPMENT

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

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

### Notes:

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

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

<sup>c</sup>First principal component of absolute latitude; climate suitability to agriculture; rate of East-West orientation; size of landmass in millions of sq km (all variables from Olsson and Hibbs 2005).
* Geographic factors influenced timing of adoption of agriculture
  Ashraf and Galor (2011)

* Quantify impact of Neolithic transition for development in 1500.

* First stage: geography ⇒ timing of transition.

* Reduced form: geography ⇒ population density 1500.

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

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

* Biogeographic factors matter through early exposure to agriculture.
# The legacy of the Neolithic transition

## GEOGRAPHY AND DEVELOPMENT IN 1500 AD

<table><thead><tr><th rowspan="3">Dependent Variable:<br>Estimator:</th><th colspan="4">Years since<br>agricultural<br>transition</th></tr><tr><th colspan="2">Population<br>density in 1500</th><th colspan="2">Population<br>density in 1500</th></tr><tr><th>OLS<br>(1)</th><th>OLS<br>(2)</th><th>OLS<br>(3)</th><th>IV<br>(4)</th></tr></thead><tbody><tr><td>Absolute latitude</td><td>-0.074<br>(3.637)***</td><td>-0.022<br>(1.411)</td><td>0.027<br>(2.373)**</td><td>0.020<br>(1.872)*</td></tr><tr><td>Percent land area in the tropics</td><td>-1.052<br>(2.356)**</td><td>0.997<br>(2.291)**</td><td>1.464<br>(3.312)***</td><td>1.636<br>(3.789)***</td></tr><tr><td>Landlocked dummy</td><td>-0.585<br>(2.306)**</td><td>0.384<br>(1.332)</td><td>0.532<br>(1.616)</td><td>0.702<br>(2.158)**</td></tr><tr><td>Island dummy</td><td>-1.085<br>(3.699)***</td><td>0.072<br>(0.188)</td><td>0.391<br>(0.993)</td><td>0.508<br>(1.254)</td></tr><tr><td>Number of annual or<br>perennial wild grasses</td><td>0.017<br>(0.642)</td><td>0.030<br>(1.105)</td><td></td><td></td></tr><tr><td>Number of domesticable<br>big mammals</td><td>0.554<br>(8.349)***</td><td>0.258<br>(3.129)***</td><td></td><td></td></tr><tr><td>Years since agricultural transition</td><td></td><td></td><td>0.426<br>(6.694)***</td><td>0.584<br>(6.887)***</td></tr><tr><td>Constant</td><td>4.657<br>(9.069)***</td><td>-0.164<br>(0.379)</td><td>-2.159<br>(4.421)***</td><td>-2.814<br>(5.463)***</td></tr><tr><td>Observations</td><td>100</td><td>100</td><td>98</td><td>98</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.707</td><td>0.439</td><td>0.393</td><td>—</td></tr></tbody></table>
* Geography might matter through institutions.

* Terrain ruggedness protected from slave trade in Africa.
    Nunn and Puga (2007)

* Reversal of fortune inconsistent with fixed geographic features having direct effects. AJR (2002)

* Geography matters through disease environment and early settlers mortality, which determined historical institutions. AJR (2001)

* Fractured land affected state formation ⇌ development.
    Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
<figure><img src="image_174.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 Log GDP per Capita in 1995
Former European Colonies

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

Source: Penn World Table 9, CEPII GeoDist. AJR (2002), 40 countries.
## Urbanization in 1500 and 1000
Former European Colonies

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

Source: AJR (2002), 30 countries.
* AJR (2001) Colonial origins: settler mortality as IV.
* AJR (2001) Colonial origins: settler mortality as IV.

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

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

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

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

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

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

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

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

⇒ Exclusion restriction invalid?

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

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

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

* Weil (2007) Health capital.

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

* K: physical capital.

* A: technology.

* H: human capital.

* Q: health capital.
<figure><img src="image_177.png" /><figcaption>GDP per Worker and Adult Survival Rate</figcaption></figure>

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

$$y_{it} = \pi x_{it} + \zeta_{i} + \mu_{t} + Z'_{it}\beta + \varepsilon_{it}$$

* y: log income per capita.

* x: log life expectancy at birth.

* ζ: country FE.

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

* Estimate long differences: panel including two dates.

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

* $t_0 = 1940$ or $t_0 = 1960$ and $t_1 = 1980$ or $t_1 = 2000$.
* Data:

* Life expectancy, demographic data. UN

* GDP per capita. Maddison
* Data:

* Life expectancy, demographic data. UN

* GDP per capita. Maddison

* Samples:

* Whole world: 120 countries.

* Base sample: 47–59 countries (reliable data).
Life Expectancy and Economic Growth

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

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

* Not driven by sample selection.

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

* Also increases ratio of population under age of 20.
Life Expectancy and Economic Growth

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

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

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

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

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

* Not driven by sample selection.

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

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

* Causal relationship? OVB and reverse causality

* ↑ growth rates + ↑ investment in health.

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

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

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

⇒ Dramatic improvement of life expectancy across world.
<figure><img src="image_178.png" /><figcaption>Log life expectancy at birth</figcaption></figure>

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

* Predicted mortality of 15 diseases before intervention.

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

* Predicted mortality of 15 diseases before intervention.

* Interaction with global intervention dates for each disease.

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

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

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

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

* Predicted mortality of 15 diseases before intervention.

* Interaction with global intervention dates for each disease.

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

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

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

* $D$: set of 15 diseases.

* $M_{di40}$: preintervention mortality.

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

⇒ $M_{it}^{I}$ is an instrument for life expectancy
# Life Expectancy and Economic Growth

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

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

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

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

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

* Threat:

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

* Include differential trends to control for this.
Life Expectancy and Economic Growth

<figure><img src="image_179.png" /><figcaption>First Stage</figcaption></figure>

Source: Acemoglu and Johnson (2007)
# Life Expectancy and Economic Growth

<figure><img src="image_180.png" /><figcaption>## First Stage: No Pre-Trends</figcaption></figure>

Source: Acemoglu and Johnson (2007)
<figure><img src="image_181.png" /><figcaption>Reduced Form: Log Population</figcaption></figure>

Source: Acemoglu and Johnson (2007)
Life Expectancy and Economic Growth

<figure><img src="image_182.png" /><figcaption>Reduced Form: Log Total GDP</figcaption></figure>

Source: Acemoglu and Johnson (2007)
Life Expectancy and Economic Growth

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

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

* Increased population somewhat increased GDP.

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

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

* Increased population somewhat increased GDP.

* But not enough to offset population growth.

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

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

* Increased population somewhat increased GDP.

* But not enough to offset population growth.

⇒ Little direct effect of geography through disease burden.

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

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

* Increased population somewhat increased GDP.

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

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

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

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

⇒ Initial life expectancy in 1940 affects subsequent growth.

* The instrument essentially measures national health in 1940.

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

⇒ FS vanishes once initial life expectancy vanishes.
Log life expectancy in 1914 and predicted mortality

<figure><img src="image_183.png" /></figure>

Source: Bloom et al. (2014)
Life Expectancy and Economic Growth

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

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

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

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

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

Fernandez-Villaverde, Koyama, Lin, and Sng (2020)

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

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

* Fractured = mountains, forests, ruggedness.

* Impedes development of large empires in Europe vs China.

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

* Too deterministic explanation?

⇒ Empirical examination
* Long-standing argument:

* States need large area of productive land for rents.

* Agricultural output needs easy appropriation.

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

* States need large area of productive land for rents.

* Agricultural output needs easy appropriation.

* Easiness of appropriation depends on geography.

* Concept of geographical core:

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

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

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

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

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
* Europe's cores:

* British Isles

* Scandinavia

* Iberian peninsula

* Italian peninsula

* Northern European plain (Poland, Germany, France)

* China's cores:

* Central-North plain

* Yunnan-Guizhou Plateau in South

* Easy connections with river system
## Study area

<figure><img src="image_185.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
* Divide space of study:

* 20,637 hexagonal cells of 28km radius.

* Each cell can sustain a polity.

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

* Area of study:

* "China" 1,434 cells.

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

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

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

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

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

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

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

* Resource availability: population density in 0 CE (Malthusian logic).
<figure><img src="image_186.png" /><figcaption>Ruggedness (standard deviation of elevation)</figcaption></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
<figure><img src="image_187.png" /><figcaption>Auxiliary barriers to conquest</figcaption></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
Population density (0 CE)

<figure><img src="image_188.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
* Time:

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

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

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

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

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
* Conquest:

* In each period, *k* is in conflict with border cell with probability α · *y*<sub>*k*</sub>, where *y*<sub>*k*</sub> is productivity (population density) of cell *k*.

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

* More productive win more often.

* Geography makes it harder or easier.

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

* Winner absorbs losing polities (single or block).

* Border cells might secede with positive probability.

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

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

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
Period 300 (1100 BCE –400 CE)

<figure><img src="image_191.png" /></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
<figure><img src="image_192.png" /><figcaption>Period 500 (1100 BCE –1400 CE)</figcaption></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
Herfindahl indices of political unity: China

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

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
<figure><img src="image_194.png" /><figcaption>Herfindahl indices of political unity: Europe</figcaption></figure>

Source: Fernandez-Villaverde, Koyama, Lin, and Sng (2020)
* Despite ruggedness, salient role of North China in fostering unification.

* Flatness of North plain facilitates military conquest.

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

* Dense European forest impeded consolidation.

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

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

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

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

* Abstract from feedback from economic growth.

⇒ Fractured land generates unification in China and persistent fragmentation in Europe.
Topic 5
Culture, institutions, and development
* Can culture affect economic development directly?

* How to separate the effects of culture and institutions?

* How do culture and institutions interact?
* Measuring culture.

* Empirical methodology to separate culture from institutions.

* Applications.
Measuring culture
Culture is the integrated pattern of human knowledge, belief, and behavior that depends upon the capacity for learning and transmitting knowledge to succeeding generations, and the customary beliefs, social forms, and material traits of a racial, religious group, or social group; and the set of shared attitudes, values, goals, and practices that characterizes an institution or organization.

(Webster Dictionary)

* Broadly, culture is a body of shared knowledge and practices.
* Need practical definition for empirical analysis.

* Identification: more useful to think about *differences in culture*.

* Differences in culture are:

* Systematic variations in preferences and beliefs.

* Across time, space, and/or social groups.

* Most common criteria: space. But not always most relevant.

* No attempt to differentiate preferences, expectations, beliefs.
  *Bicchieri (2006, 2016)*
## Alternative approach:

* Culture as a focal factor. Myerson (2009)

* Social structures characterized as a game with multiple equilibria.

* Culture is a mechanism of equilibrium selection and coordination.

* No need for differences in preferences.
## Alternative approach:

* Culture as a focal factor. Myerson (2009)

* Social structures characterized as a game with multiple equilibria.

* Culture is a mechanism of equilibrium selection and coordination.

* No need for differences in preferences.

* Related to the emergence of culture?

* Challenging to provide empirical evidence (analytical narratives).
* World-wide:

* World Values Survey: 6 waves, 1981–2014, up to 60 countries.

* International Social Survey: 4 waves, 1985–2016, up to 43 countries.

* Global Preferences Survey: 76 countries (2012). Falk et al. (2018)

* Regional:

* European Values Study: 4 waves, 1981–2009, up to 47 countries.

* Demographic and Health Surveys: 1985–2017, 90 countries.

* Afro Barometer (1999–2019), Latino Barometer (1995–2017), Asian Barometer (2001–2016).

* Country:

* United States: General Social Survey (1976–2017).

* France: DREES, ECAM, CREDOC...
* Trust.

* Gender roles.

* Time preference.

* Individualism vs collectivism.

* Family ties.

* Morality.

* Work and Poverty.
# Measurement Challenges: Data Availability

* Across countries.

* WVS: 60 countries in wave 6 (2010–2014).

* GPS: 76 countries, 90% of world population.

* Cultures as units matter.

* Across time.

* WVS: wave 1 (1981–1984), 10 countries.

* GSS: since 1976, but only for the U.S.

* Culture is a long-run process, changes over multiple generations.

* Within countries.

* WVS: wave 6 (2010–2014), average 13 regions per country.

* GPS: state/province of residence.

* Usually: no precise residence location, no birth location.
Data Availability across Countries

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

* Share approves of "When jobs are scarce, men should have more right to a job than women" across 58 countries WVS, Wave 6, 2010–2014
# Measurement Challenges: Aggregation Issues

* Question level:

* "When jobs are scarce, men should have more right to a job than women"

* Agree: $1→1$.

* Neither: $2→0.5$.

* Disagree: $3→0$.

* "Being a housewife is just as fulfilling as working for pay"

* Strongly agree: $1→1$.

* Agree: $2→0.66$.

* Disagree: $3→0.33$.

* Strongly disagree: $4→0$.

* Several issues with Likert scales.

* Index level: linear, PCA, ...?

* Country level: flat or sample weights?
* What is measured?

* Expectations, beliefs (1st, 2nd order), preferences, attitudes?

* GPS: cross-validated with experiments (but rare).

* Comparing cultures

* Do universal categories of culture exist?

* Cultural biases (ethnocentrism).
* Past behavior e.g. past FLFP

* Captures culture + economic and institutional conditions.

* Data comparability and coverage.

* (Deep) historical measures

* E.g. Pre-modern division of labor, language structures.

* Fixed over time.

* Measurement issues.

* Little within-country variation.

* Assumptions on historical processes (persistence, transmission).
Empirical methodology to separate culture from institutions
Correlation between Attitudes and FLFP

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

* Values: share approves of “When jobs are scarce, men should have more right to a job than women.” WVS, Wave 6, 2010–2014
* Outcome: FLFP 15–65 (2010–2014). ILO

* Cultural variable: approves of “When jobs are scarce, men should have more right to a job than women” WVS

* (Very rough) controls (more later): area, GDP, schooling, fertility, migration, population.

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">FLFP 15–65</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>WVS question (0–1)</td><td>-0.61***<br>[0.08]</td><td>-0.82***<br>[0.16]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Continent FE</td><td>No</td><td>Yes</td></tr><tr><td>Countries</td><td>51</td><td>51</td></tr><tr><td>Adj. R2</td><td>0.477</td><td>0.502</td></tr></tbody></table>
Results lack credibility:

* Reverse causality.

* Endogeneity with economic incentives and institutional structures.

* Few countries available in surveys.

* No data for past attitudes.
* Identify culture by fixing economic and institutional factors.

Fernández (2011)
* Identify culture by fixing economic and institutional factors.
  Fernández (2011)

* Compare individuals

* Within same institutional and economic environment.

* Whose preferences and beliefs might differ systematically.
* Identify culture by fixing economic and institutional factors.
  Fernández (2011)

* Compare individuals

* Within same institutional and economic environment.

* Whose preferences and beliefs might differ systematically.

* Compare movers (1st, 2nd, higher order generations).

* (Testable) assumptions:

* Cultural beliefs persist across generations.

* Cultural beliefs vary systematically across groups (spatially defined?).

* Individuals in the same area face identical institutional environment.
* Origin culture might attenuates over time.

* Institutional environment might vary within host environment.

* Movers are a selected sample.
* Gender roles and married women LFP.

* Married women $i$ from country $k$ maximizes:

$$U(c, v_i) = u(c) - 1v_i$$

* $u(\cdot)$: strictly increasing concave utility function.

* $1$: indicator for working.

* $c$: household consumption.

* $v_i$: disutility from working.

$$c = w_{hk} + 1w_{fk}$$

* $w_F$: wife labor income if works.

* $w_h$: husband's income (always works).
# The Epidemiological Approach: A Simple Model

## Sources of heterogeneity:

* **Wages:**

* Identical and exogenous within countries.

* (Potentially) heterogeneous across countries and sex.

* **Disutility of work:**

* Drawn from country-specific distribution $G_k(m_k, \sigma)$.

* Varies across women within a country.

* Varies across countries.

* **Differences in culture:** differences in mean $m_k$.

* **Approximation:** country ≈ culture.
* $L_k$: FLFP in country k is given by

$$L_k = \Pr(v_k \le v_k^*) = G_k(v_k^*)$$
with $v_k^* = v^*(w_{hk}, w_{fk}) = u(w_{hk} + w_{fk}) - u(w_{hk})$

* Suppose $G \sim \mathcal{N}(m_k, \sigma^2)$. Then:

$$L_k = G_k(v_k^*) = \Phi\left(\frac{v_k^* - m_k}{\sigma}\right)$$
* Culture matters through $m_k$: $↑ m_k \Rightarrow ↓ L_k$.

$$ \frac{\partial L_k}{\partial m_k} = -\frac{1}{\sigma} \phi \left( \frac{v_k^* - m_k}{\sigma} \right) < 0 $$

* Institutional framework matters through wages and thus $v_k^*$.

$$ \frac{\partial L_k}{\partial w_{fk}} = \frac{1}{\sigma} \phi \left( \frac{v_k^* - m_k}{\sigma} \right) u' (w_{hk} + w_{fk}) > 0 $$

$$ \frac{\partial L_k}{\partial w_{hk}} = \frac{1}{\sigma} \phi \left( \frac{v_k^* - m_k}{\sigma} \right) [u' (w_{hk} + w_{fk}) - u' (w_{hk})] < 0 $$
* Women from different countries in same environment and same husbands:

* Same $w_{hi}$ and same $w_{fi}$.

* Same threshold $v^*$.

* But draw from different distributions $G_k$.

* (Assumption: culture persists for higher order generations of movers.)

* Proportion of women from (ancestry) k who work in country j is:

$$L_{kj} = \Phi \left( \frac{v_j^* - m_k}{\sigma} \right)$$

* Structural estimation approach left aside

Fernández (2013)

Fogli and Veldkamp (2011)
Cultural differences $m_k$ not observable, so need a proxy.

* Measure of attitudes: measurement issues.

* Economic behaviors directly.

* Reference group behavior e.g ancestry FLFP.

* Captures culture + economic and institutional conditions.

* Leverage economic and institutional conditions are fixed (same $v^*$).

* Left with cultural component.

* (Relatively) easy to implement.

* (Deep) historical measures:

* Measurement challenges.

* Potentially (much more) meaningful for identification.
Further issues common to all measures of culture:

* Other (endogenous) sources of heterogeneity beyond culture.

* Proxy might capture inherited social capital.

* Which dimension of culture is captured?
* Case: FLFP of Immigrants to the United States.

* Fernández and Fogli (2009) Blau (2015)

* Data: ACS 2005–2017 IPUMS

* Sample selection:

* Born outside the U.S. from non US parents.

* Age: 30–49.

* Regular household, no farms, married spouse present.

* Born in single identified country. BPL

⇒ 268k women from 109 countries.
* Proxy for country of birth culture: ratio FLFP to MLFP age 15–65.

* Data: ILO Estimates

* Assignment: year of migration.

* Time coverage for migration year: 1990–2017.
* Controls to build comparison groups.
Application: Gender Roles and FLFP

* Controls to build comparison groups.

* Survey year: 13 categories.

* Individual characteristics:

* Cohort: 32 categories (1956–1987).

* Education: 4 categories (below HS, HS, College, College+). EDUC

* English: 3 categories (No English, some, English+). SPEAKENG

* Ethnicity: 4 categories (White, Black, Asian, Other). RACE

* Year immigration: 28 categories (1990–2017).

* Number of children (in household): 0–9.

* State of residence: 50 categories.
* Controls to build comparison groups.

* Survey year: 13 categories.

* Individual characteristics:

* Cohort: 32 categories (1956–1987).

* Education: 4 categories (below HS, HS, College, College+). EDUC

* English: 3 categories (No English, some, English+). SPEAKENG

* Ethnicity: 4 categories (White, Black, Asian, Other). RACE

* Year immigration: 28 categories (1990–2017).

* Number of children (in household): 0–9.

* State of residence: 50 categories.

* Spouse characteristics:

* Same as above.

* Employment status (indicator).

* Citizenship status (indicator).

* Total income in 1999 US$.
* COB geography:

* Area, distance, continent, landlocked. CEPII

* COB history:

* Identity of colonizer: 14 categories. CEPII

* Genetic distance to US. Spolaore and Wacziarg (2018)

* COB economy:

* Real GDP PPP 2011 US$. PWT

* Population. PWT

* Female years of schooling and female/male ratio. Barro and Lee (2013)

* Total fertility rate. UN DESA

* Net migration rate. UN DESA
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>COB M/F LFP 15-65</td><td>0.31***<br>[0.05]</td><td>0.24***<br>[0.05]</td><td>0.33***<br>[0.09]</td><td>0.33***<br>[0.06]</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td></tr><tr><td>State and year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual and Spouse</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>COB</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>COB</td><td>109</td><td>109</td><td>109</td><td>109</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr><tr><td>Adj. R2</td><td>0.024</td><td>0.093</td><td>0.049</td><td>0.106</td></tr></tbody></table>

Outcome: mean = 0.57, s.d. = 0.13

COB proxy: mean = 0.69, s.d. = 0.21
* ↑ 1pp COB FLFP associated with ↑ 0.3pp Active.

* ↑ 10pp COB FLFP associated with ↑ 6% of mean in Active

* ↑ 1 s.d. COB FLFP associated with ↑ 0.5 s.d. in Active.
* ↑ 1pp COB FLFP associated with ↑ 0.3pp Active.

* ↑ 10pp COB FLFP associated with ↑ 6% of mean in Active

* ↑ 1 s.d. COB FLFP associated with ↑ 0.5 s.d. in Active.

* Spirit of this method used to identify cultural effects.
Applications
Similar strategy to assess role of culture for economic outcomes (1/2).

* Divorce rates Furtado, Marcén, and Sevilla (2013)

* Self-employment Marcén (2014)

* Living arrangements Giuliano (2007) Marcén and Morales (2018)

* Fertility Bellido et. al (2016) Stichnoth and Yeter (2016)
  Marcén et. al (2018) Salari (2018)

* Son preference Almond, Edlund, and Milligan (2013)

* Savings rates Costa-Font et al. (2018)
# General Applications

Similar strategy to assess role of culture for economic outcomes (2/2).

* Mortgage finance Rodríguez-Planas (2018)

* Home-ownership Marcén and Morales (2019)

* Unemployment rates Brugger et al. (2009)

* Division of labor within family Frank and Hou (2015)

* Science education Nollenberger et al. (2016)
  Rodríguez-Planas and Nollenberger (2018)

* Human capital accumulation Hoorn (2019)

* Health behavior Rodríguez-Planas and Sanz-de-Galdeano (2019)

* Preference for redistribution Luttmer and Singhal (2011)
* Most useful to study bigger questions with historical perspective:

* How can we explain observed differences in culture?

* Mechanisms through which culture affects behavior?

* How does culture transmit across generations?

* When does culture persist over time? When doesn't it?

* What are the mechanisms of cultural change?

* How does culture emerge?
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

* Explain contemporaneous distribution of trust in Africa: the slave trade. Nunn and Wantchekon (2011)

* Explain contemporaneous distribution of economic status in Africa: ancestral lifeways. Michalopoulos, Putterman and Weil (2019)

* Mechanisms of persistence of historical shocks: contemporaneous gender norms in Africa and the slave trade. Teso (2019)
THE
QUARTERLY JOURNAL
OF ECONOMICS

Vol. 128

May 2013

Issue 2

ON THE ORIGINS OF GENDER ROLES: WOMEN AND THE PLOUGH*

ALBERTO ALESINA
PAOLA GIULIANO
NATHAN NUNN
<figure><img src="image_197.png" /></figure>

Source: WVS, ILO.
* How can we explain current dispersion in norms about gender roles?
* How can we explain current dispersion in norms about gender roles?

* Hypothesis of Boserup (1970):

* Form of traditional agriculture in pre-industrial societies.

* Shifting versus plough.

* Plough: capital intensive, requiring more strength to control animal.

* Men have comparative advantage in farming.

⇒ Specialization along gender lines ⇒ gender norms.
* How can we explain current dispersion in norms about gender roles?

* Hypothesis of Boserup (1970):

* Form of traditional agriculture in pre-industrial societies.

* Shifting versus plough.

* Plough: capital intensive, requiring more strength to control animal.

* Men have comparative advantage in farming.

⇒ Specialization along gender lines ⇒ gender norms.

* Generated norms about gender roles that persisted:

* Underlying trait reinforced by institutions.

* Complementarities between cultural beliefs and industrial structure.

* Cultural beliefs are inherently sticky.
Does traditional plough use have a causal impact on subsequent cultural norms?
* Murdock's *Ethnographic Atlas*.

* Information for 1,265 ethnic groups (before European contact).

* Widely used but source criticism needed.

Giuliano and Nunn (2018) Giuliano and Matranga (2020)

* Historical plough use:

① Absent (plough = 0)

② Existed but not aboriginal (plough = 1)

③ Aboriginal (plough = 1)
* Traditional female participation in agriculture:

① Males only (Female specialization = 1)

② Males appreciably more (Female specialization = 2)

③ Equal participation (Female specialization = 3)

④ Females appreciably more (Female specialization = 4)

⑤ Females only (Female specialization = 5)

* Presence of domesticated animals.

* Density of ethnic groups' settlements.

* Political complexity.
* Geographic coordinates of centroid of historical group.

* Land suitability for cultivation of crops (FAO).

* Tropical or subtropical characteristics.
# Historical Impact of Traditional Plough Agriculture

## Traditional plough use and female participation in pre-industrial agriculture

<table><thead><tr><th rowspan="2"></th><th colspan="6">Dependent variable: Traditional participation of females relative to males in the following tasks:</th></tr><tr><th>Overall agriculture</th><th>Land clearance</th><th>Soil preparation</th><th>Planting</th><th>Crop tending</th><th>Harvesting</th></tr></thead><tbody><tr><th>Mean of dep. var.</th><td>3.04</td><td>2.83</td><td>1.45</td><td>2.15</td><td>2.86</td><td>3.16</td><td>3.23</td></tr><tr><th>Traditional plough agriculture</th><td>-0.883***<br>(0.225)</td><td>-1.136***<br>(0.240)</td><td>-0.434**<br>(0.197)</td><td>-1.182***<br>(0.320)</td><td>-1.290***<br>(0.306)</td><td>-1.188***<br>(0.351)</td><td>-0.954***<br>(0.271)</td></tr><tr><th>Ethnographic controls</th><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><th>Observations</th><td>660</td><td>124</td><td>129</td><td>124</td><td>131</td><td>122</td><td>131</td></tr><tr><th>Adjusted R-squared</th><td>0.13</td><td>0.19</td><td>0.14</td><td>0.10</td><td>0.09</td><td>0.13</td><td>0.16</td></tr><tr><th>R-squared</th><td>0.14</td><td>0.23</td><td>0.18</td><td>0.14</td><td>0.13</td><td>0.18</td><td>0.20</td></tr></tbody></table>

s.d. outcome in (1) is 1.0
# Historical Impact of Traditional Plough Agriculture

## Traditional plough use and female participation outside agriculture

<table><thead><tr><th rowspan="2"></th><th colspan="9">Dependent variable: Traditional participation of females relative to males in the following tasks:</th></tr><tr><th>(1)<br>Caring<br>for small<br>animals</th><th>(2)<br>Caring<br>for large<br>animals</th><th>(3)<br>Milking</th><th>(4)<br>Cooking</th><th>(5)<br>Fuel<br>gathering</th><th>(6)<br>Water<br>fetching</th><th>(7)<br>Burden<br>carrying</th><th>(8)<br>Handicrafts</th><th>(9)<br>Trading</th></tr></thead><tbody><tr><td>Mean of dep. var.</td><td>3.53</td><td>1.73</td><td>3.25</td><td>4.65</td><td>3.90</td><td>4.64</td><td>3.47</td><td>2.78</td><td>2.47</td></tr><tr><td>Traditional plough use</td><td>0.14<br>(0.517)</td><td>0.064<br>(0.254)</td><td>0.63<br>(0.697)</td><td>-0.019<br>(0.108)</td><td>-0.638<br>(0.403)</td><td>-0.052<br>(0.205)</td><td>-0.962**<br>(0.378)</td><td>-0.157<br>(0.274)</td><td>-0.155<br>(0.542)</td></tr><tr><td>Ethnographic controls</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Observations</td><td>88</td><td>95</td><td>48</td><td>173</td><td>159</td><td>154</td><td>135</td><td>74</td><td>59</td></tr><tr><td>Adjusted R-squared</td><td>-0.02</td><td>-0.02</td><td>0.03</td><td>0.01</td><td>-0.001</td><td>0.01</td><td>0.12</td><td>0.07</td><td>-0.01</td></tr><tr><td>R-squared</td><td>0.05</td><td>0.04</td><td>0.14</td><td>0.04</td><td>0.04</td><td>0.04</td><td>0.16</td><td>0.15</td><td>0.10</td></tr></tbody></table>
* Data:

* Use *Ethnologue* for location of language groups.

* Use *Landscan 2000* for world's population by 1 km² grid cell.

* Combine for distribution of language groups today.

* Match with *Ethnographic Atlas* plough data by grid cell.

* Aggregate at country or district level.

* Main variable: fraction of location with ancestors traditionally using plough.
Language groups and historical plough use in Ethiopia

<figure><img src="image_198.png" /></figure>
## Traditional plough use among ethnic groups

<figure><img src="image_199.png" /></figure>
* Contemporaneous outcomes:

* FLFP in 2000 (World Bank).

* Proportion of firms owned by women (World Bank).

* Share women in Parliament (UN).
* Contemporaneous outcomes:

* FLFP in 2000 (World Bank).

* Proportion of firms owned by women (World Bank).

* Share women in Parliament (UN).

* OLS regression:

$$y_c = \alpha + \beta \text{Plough}_c + \mathbf{X}_c^H \Gamma + \mathbf{X}_c^C \Pi + \varepsilon_c$$

* $\mathbf{X}_c^H$: historical ethnographic controls (domesticated animals, density of settlements, tropical climate, ...).

* $\mathbf{X}_c^C$: contemporaneous controls (GDP per capita, flexible).
<figure><img src="image_200.png" /><figcaption>Partial Correlation: Plough and FLFP</figcaption></figure>
<figure><img src="image_201.png" /><figcaption>Partial Correlation: Plough and Ownership</figcaption></figure>
Partial Correlation: Plough and Politics

<figure><img src="image_202.png" /></figure>
# Country-Level Estimates: Magnitudes

* Summary statistics: mean (s.d.)

* Plough: 0.53 (0.47).

* FLFP: 51.3 (15.6).

* Ownership: 35.2 (15.0).

* Politics: 11.8 (8.9).
# Country-Level Estimates: Magnitudes

* Summary statistics: mean (s.d.)

* Plough: 0.53 (0.47).

* FLFP: 51.3 (15.6).

* Ownership: 35.2 (15.0).

* Politics: 11.8 (8.9).

* Interpretation in means:

* ↑ 1 s.d. plough associated with ↓ 11% FLFP mean.

* ↑ 1 s.d. plough associated with ↓ 20% ownership mean.

* ↑ 1 s.d. plough associated with ↓ 19% politics mean.
* Summary statistics: mean (s.d.)

* Plough: 0.53 (0.47).

* FLFP: 51.3 (15.6).

* Ownership: 35.2 (15.0).

* Politics: 11.8 (8.9).

* Interpretation in means:

* ↑ 1 s.d. plough associated with ↓ 11% FLFP mean.

* ↑ 1 s.d. plough associated with ↓ 20% ownership mean.

* ↑ 1 s.d. plough associated with ↓ 19% politics mean.

* Interpretation in s.d.:

* ↑ 1 s.d. plough associated with ↓ 0.38 FLFP s.d.

* ↑ 1 s.d. plough associated with ↓ 0.48 ownership s.d.

* ↑ 1 s.d. plough associated with ↓ 0.25 politics s.d.
* Summary statistics: mean (s.d.)

* Plough: 0.53 (0.47).

* FLFP: 51.3 (15.6).

* Ownership: 35.2 (15.0).

* Politics: 11.8 (8.9).

* Interpretation in means:

* ↑ 1 s.d. plough associated with ↓ 11% FLFP mean.

* ↑ 1 s.d. plough associated with ↓ 20% ownership mean.

* ↑ 1 s.d. plough associated with ↓ 19% politics mean.

* Interpretation in s.d.:

* ↑ 1 s.d. plough associated with ↓ 0.38 FLFP s.d.

* ↑ 1 s.d. plough associated with ↓ 0.48 ownership s.d.

* ↑ 1 s.d. plough associated with ↓ 0.25 politics s.d.

* Based on R2, explains 6% of FLFP, 11% of ownership, 3% of politics.
* Individual-level outcomes from WVS:

* Indicator for participation in labor force.

* Attitudes toward work ("When jobs scarce, men have priority").

* Attitudes toward politics ("Men better politicians").
* Individual-level outcomes from WVS:

* Indicator for participation in labor force.

* Attitudes toward work ("When jobs scarce, men have priority").

* Attitudes toward politics ("Men better politicians").

* OLS regression:

$$y_{i,d,c} = \alpha_c + \beta \text{Plough}_d + \mathbf{X}_d^H \mathbf{\Pi} + \mathbf{X}_i \mathbf{\Phi} + \varepsilon_{i,d,c}$$

* $\alpha_c$: country FE.

* $\mathbf{X}_d^H$: historical ethnographic controls in district $d$, country $c$.

* $\mathbf{X}_i$: individual controls (age, marital status, gender, education).
# Subnational Estimates 1

## Individual-level OLS estimates using WVS data

<table><thead><tr><th rowspan="2"></th><th rowspan="2">(1)</th><th rowspan="2">(2)</th><th rowspan="2">(3)</th><th rowspan="2">(4)</th><th rowspan="2">(5)</th><th rowspan="2">(6)</th></tr><tr><th colspan="4">Dependent variable:</th></tr><tr><th></th><th>Female labor force participation, 1995-2007</th><th>When jobs are scarce, 1995-2007</th><th>Men better political leaders, 1995-2007</th><th></th><th></th><th></th></tr></thead><tbody><tr><th>Mean of dep. var.</th><td>0.55</td><td>0.55</td><td>0.46</td><td>0.47</td><td>2.62</td><td>2.64</td></tr><tr><th>Traditional plough use</th><td>-0.177***<br>(0.035)</td><td>-0.002<br>(0.031)</td><td>0.193***<br>(0.033)</td><td>0.100*<br>(0.059)</td><td>0.224***<br>(0.069)</td><td>0.304***<br>(0.117)</td></tr><tr><th>Individual & district controls</th><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><th>Contemporary country controls</th><td>yes</td><td>n/a</td><td>yes</td><td>n/a</td><td>yes</td><td>n/a</td></tr><tr><th>Fixed effects</th><td>continent</td><td>country</td><td>continent</td><td>country</td><td>continent</td><td>country</td></tr><tr><th>Number of countries</th><td>73</td><td>78</td><td>74</td><td>79</td><td>50</td><td>55</td></tr><tr><th>Number of districts</th><td>672</td><td>698</td><td>674</td><td>700</td><td>453</td><td>479</td></tr><tr><th>Observations</th><td>43,801</td><td>47,587</td><td>80,303</td><td>87,528</td><td>64,215</td><td>72,152</td></tr><tr><th>Adjusted R-squared</th><td>0.17</td><td>0.27</td><td>0.21</td><td>0.28</td><td>0.19</td><td>0.26</td></tr><tr><th>R-squared</th><td>0.17</td><td>0.27</td><td>0.21</td><td>0.28</td><td>0.19</td><td>0.26</td></tr></tbody></table>
* Individual-level outcomes from IPUMS-I:

* Indicator for participation in labor force.

* 8 countries with ethnic identity information.
* Individual-level outcomes from IPUMS-I:

* Indicator for participation in labor force.

* 8 countries with ethnic identify information.

* OLS regression:

$$y_{i,d,e} = \alpha_d + \beta \text{Plough}_e + \mathbf{X}_e^H \mathbf{\Pi} + \mathbf{X}_i \mathbf{\Phi} + \varepsilon_{i,d,e}$$

* $\alpha_d$: district FE.

* $\mathbf{X}_e^H$: historical ethnographic controls in district d, ethnicity e.

* $\mathbf{X}_i$: individual controls (age, marital status, gender, education).
Subnational Estimates 2

Individual-level OLS estimates using IPUMS-I data

<table><thead><tr><th rowspan="2"></th><th rowspan="2">(1)</th><th rowspan="2">(2)</th><th rowspan="2">(3)</th><th rowspan="2">(4)</th><th colspan="5">Dependent variable: Female labor force participation indicator</th><th rowspan="2">(9)</th></tr><tr><th>Bolivia<br>2001</th><th>Chile,<br>2002</th><th>Cambodia,<br>2008</th><th>Malaysia,<br>70, 80, 91, 00</th><th>Mongolia,<br>1989, 2000</th><th>Nepal,<br>2001</th><th>Philippines,<br>1990</th><th>Uganda,<br>1991, 2002</th><th>All<br>countries</th></tr></thead><tbody><tr><td>Mean of dep. var.</td><td>0.44</td><td>0.40</td><td>0.78</td><td>0.40</td><td>0.38</td><td>0.54</td><td>0.39</td><td>0.56</td><td>0.49</td><td></td></tr><tr><td>Traditional plough use</td><td>-0.035***<br>(0.002)</td><td>-0.073***<br>(0.003)</td><td>-0.064**<br>(0.027)</td><td>-0.080***<br>(0.016)</td><td>-0.006<br>(0.013)</td><td>-0.100**<br>(0.043)</td><td>0.035<br>(0.023)</td><td>-0.079***<br>(0.020)</td><td>-0.040**<br>(0.019)</td><td></td></tr><tr><td>Individual &amp; ethnicity<br>controls</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td></td></tr><tr><td>District fixed effects</td><td>9</td><td>26</td><td>24</td><td>15</td><td>23</td><td>14</td><td>77</td><td>4</td><td>192</td><td></td></tr><tr><td>Ethnic groups</td><td>6</td><td>5</td><td>11</td><td>21</td><td>10</td><td>16</td><td>21</td><td>60</td><td>150</td><td></td></tr><tr><td>Observations</td><td>173,804</td><td>505,114</td><td>432,481</td><td>319,580</td><td>125,349</td><td>710,662</td><td>1,266,363</td><td>1,003,321</td><td>4,536,674</td><td></td></tr><tr><td>Adjusted R-squared</td><td>0.07</td><td>0.17</td><td>0.19</td><td>0.10</td><td>0.50</td><td>0.19</td><td>0.13</td><td>0.09</td><td>0.15</td><td></td></tr><tr><td>R-squared</td><td>0.07</td><td>0.17</td><td>0.19</td><td>0.10</td><td>0.50</td><td>0.19</td><td>0.13</td><td>0.09</td><td>0.15</td><td></td></tr></tbody></table>
Country-Level Estimates: 2SLS

Countr-level 2SLS estimates

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td colspan="9">Panel A. First stage 2SLS estimates. Dependent variable: Traditional plough use</td></tr><tr><td>Mean of dep. var.</td><td>0.53</td><td>0.44</td><td>0.54</td><td>0.51</td><td></td><td></td><td></td><td></td></tr><tr><td>Plough-positive environment</td><td>0.744***<br>(0.084)</td><td>0.629***<br>(0.089)</td><td>0.861***<br>(0.078)</td><td>0.673***<br>(0.103)</td><td>0.820***<br>(0.082)</td><td>0.685***<br>(0.104)</td><td>0.874***<br>(0.089)</td><td>0.717***<br>(0.118)</td></tr><tr><td>Plough-negative environment</td><td>0.119<br>(0.122)</td><td>0.185<br>(0.133)</td><td>0.100<br>(0.166)</td><td>0.115<br>(0.171)</td><td>0.132<br>(0.130)</td><td>0.187<br>(0.141)</td><td>0.129<br>(0.181)</td><td>0.142<br>(0.188)</td></tr><tr><td>Equality of coefficients (p-value)</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>F-stat (plough variables)</td><td>40.21</td><td>25.06</td><td>66.80</td><td>21.88</td><td>51.96</td><td>21.88</td><td>49.54</td><td>18.52</td></tr></tbody></table>
Country-level 2SLS estimates

<table><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable (panels B & C):</th></tr><tr><th>Female labor force participation in 2000</th><th>Share of firms with female ownership, 2005-2011</th><th>Share of political positions held by women in 2000</th><th>Average effect size (AES)</th></tr></thead><tbody><tr><td>Plough-negative environment</td><td>18.928***<br>(6.506)</td><td>6.072<br>(9.926)</td><td>9.134<br>(10.401)</td><td>2.975<br>(6.093)</td><td>0.607<br>(0.391)</td><td>0.653*<br>(0.393)</td></tr><tr><td>Equality of coefficients (p-value)</td><td>0.00<br>14.87</td><td>0.00<br>12.49</td><td>0.02<br>5.41</td><td>0.56<br>4.46</td><td>0.00<br>9.19</td><td>0.00<br>7.11</td></tr><tr><td colspan="7">Panel C. Second-stage 2SLS estimates</td></tr><tr><td>Traditional plough use</td><td>-21.630***<br>(5.252)</td><td>-25.013***<br>(7.513)</td><td>-17.486***<br>(5.533)</td><td>-22.689***<br>(7.620)</td><td>-6.460***<br>(2.334)</td><td>-9.726***<br>(3.750)</td><td>-0.918***<br>(0.225)</td><td>-1.313***<br>(0.388)</td></tr><tr><td>Hausman test (p-value)</td><td>0.02<br>0.00</td><td>0.04<br>0.00</td><td>0.56<br>0.41</td><td>0.40<br>0.31</td><td>0.22<br>0.72</td><td>0.10<br>0.86</td><td>0.33<br>0.05</td><td>0.16<br>0.06</td></tr><tr><td>Hansen J</td><td>yes<br>no</td><td>yes<br>yes</td><td>yes<br>no</td><td>yes<br>yes</td><td>yes<br>no</td><td>yes<br>yes</td><td>yes<br>no</td><td>yes<br>yes</td></tr><tr><td>Historical & contemporary controls</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Continent PEs</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>160</td><td>160</td><td>122</td><td>122</td><td>140</td><td>140</td><td>104</td><td>104</td></tr></tbody></table>
* Separate cultural transmission from correlated effects from institutions.
* Separate cultural transmission from correlated effects from institutions.

* Country FE with WVS and district FE with IPUMS first step.
* Separate cultural transmission from correlated effects from institutions.

* Country FE with WVS and district FE with IPUMS first step.

* Epidemiological approach on 2nd generation immigrants within U.S. (CPS) and Europe (ESS).
* Separate cultural transmission from correlated effects from institutions.

* Country FE with WVS and district FE with IPUMS first step.

* Epidemiological approach on 2nd generation immigrants within U.S. (CPS) and Europe (ESS).

* OLS regression on daughters of immigrants, aged 15–64:

$$y_{i,s,c} = \alpha_s + \beta \text{Plough}_c + \mathbf{X}_c^\text{C} \Gamma + \mathbf{X}_d^\text{H} \Pi + \mathbf{X}_i^\text{F} \Phi + \varepsilon_{i,s,c}$$

* $\alpha_s$: state FE.

* $\mathbf{X}_c^\text{H}$: historical ethnographic controls (domesticated animals, density of settlements, tropical climate, ...).

* $\mathbf{X}_c^\text{C}$: contemporaneous controls (GDP per capita, flexible).

* $\mathbf{X}_i$: individual controls (age, marital status, gender, education).
Determinants of FLFP for US children of immigrants

<table><thead><tr><th rowspan="3"></th><th colspan="9">Dependent variable: Labor force participation indicator, 1994–2011</th></tr><tr><th colspan="3">All women</th><th colspan="3">Married women</th><th colspan="3">Husband's ancestry</th></tr><tr><th colspan="3">Woman's ancestry</th><th colspan="3">Woman's ancestry</th><th colspan="3">Husband's ancestry</th></tr><tr><th></th><th>Father's country</th><th>Mother's country</th><th>Parents same country</th><th>Father's country</th><th>Mother's country</th><th>Parents same country</th><th>Father's country</th><th>Mother's country</th><th>Parents same country</th></tr></thead><tbody><tr><td>Mean of dep. var.</td><td>0.63</td><td>0.63</td><td>0.60</td><td>0.68</td><td>0.69</td><td>0.69</td><td>0.70</td><td>0.71</td><td>0.70</td></tr><tr><td>Traditional plough use</td><td>-0.044***<br>(0.015)</td><td>-0.043**<br>(0.018)</td><td>-0.062***<br>(0.020)</td><td>-0.094**<br>(0.046)</td><td>-0.118***<br>(0.043)</td><td>-0.136**<br>(0.054)</td><td>-0.065***<br>(0.024)</td><td>-0.045**<br>(0.022)</td><td>-0.058**<br>(0.024)</td></tr><tr><td>Observations</td><td>57,138</td><td>55,341</td><td>32,776</td><td>10,206</td><td>9,508</td><td>6,835</td><td>35,393</td><td>35,158</td><td>23,124</td></tr><tr><td>Adjusted R-squared</td><td>0.23</td><td>0.23</td><td>0.25</td><td>0.10</td><td>0.10</td><td>0.11</td><td>0.08</td><td>0.08</td><td>0.08</td></tr><tr><td>R-squared</td><td>0.23</td><td>0.23</td><td>0.26</td><td>0.11</td><td>0.11</td><td>0.12</td><td>0.09</td><td>0.08</td><td>0.09</td></tr></tbody></table>

* Cultural transmission accounts for 35–50% of total effect.
Determinants of gender attitudes of European children of immigrants

<table><thead><tr><th rowspan="3"></th><th colspan="6">(1)</th></tr><tr><th colspan="6">(2)</th></tr><tr><th colspan="6">(3)</th></tr><tr><th colspan="6">Dependent variables: "When jobs are scarce..." survey response, 2004-2011</th></tr><tr><th></th><th colspan="2">Father's country</th><th colspan="2">Mother's country</th><th colspan="2">Same country</th></tr><tr><th></th><th>1-5 scale</th><th>Indicator</th><th>1-5 scale</th><th>Indicator</th><th>1-5 scale</th><th>Indicator</th></tr></thead><tbody><tr><th>Mean of dep. var.</th><td>2.54</td><td>0.32</td><td>2.53</td><td>0.32</td><td>2.62</td><td>0.35</td></tr><tr><th>Traditional plough use</th><td>0.219**<br>(0.091)</td><td>0.073**<br>(0.034)</td><td>0.214**<br>(0.086)</td><td>0.070**<br>(0.033)</td><td>0.298***<br>(0.096)</td><td>0.094**<br>(0.038)</td></tr><tr><th>Observations</th><td>15,545</td><td>13,024</td><td>15,260</td><td>12,788</td><td>10,535</td><td>8,780</td></tr><tr><th>Adjusted R-squared</th><td>0.18</td><td>0.16</td><td>0.17</td><td>0.16</td><td>0.17</td><td>0.16</td></tr><tr><th>R-squared</th><td>0.18</td><td>0.17</td><td>0.17</td><td>0.16</td><td>0.17</td><td>0.17</td></tr></tbody></table>

* Cultural transmission accounts for 36-49% of total effect.
American Economic Review 101 (December 2011): 3221–3252

http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.7.3221

The Slave Trade and the Origins of Mistrust in Africa®

By NATHAN NUNN AND LEONARD WANTCHEKON®
* How can we explain current dispersion of trust in Africa?
* How can we explain current dispersion of trust in Africa?

* Why did Africa's slave trade have detrimental effects on economic development? Nunn (2008)
* How can we explain current dispersion of trust in Africa?

* Why did Africa's slave trade have detrimental effects on economic development? Nunn (2008)

* Hypothesis:

* Slave trade = environment of ubiquitous insecurity.

* Individuals turned on others to sell each other into slavery (including friends and family) through kidnapping or trickery.

* A culture of mistrust might have developed because higher returns (Boyd and Richerson, 1985).

* This cultural trait then persisted (stickiness, complementarities with institutions, self-reinforcement).
* Contemporaneous data on trust:

* Individual-level data Afrobarometer 2005 across 17 sub-Saharan countries.

* 21,000 individuals with identified ethnicity.

* Questions about trust in relatives, neighbors, local government, own and other ethnic groups.
* Contemporaneous data on trust:

* Individual-level data Afrobarometer 2005 across 17 sub-Saharan countries.

* 21,000 individuals with identified ethnicity.

* Questions about trust in relatives, neighbors, local government, own and other ethnic groups.

* Historical data on slave exports:

* Number of slaves taken from each ethnic group and country from Nunn (2008).

* Four slave trades 1400–1900.

* Matching slave ethnicities to Afrobarometer ethnies through Murdock Atlas.
<figure><img src="image_203.png" /><figcaption>Transatlantic slave trade</figcaption></figure>
<figure><img src="image_204.png" /><figcaption>Indian Ocean slave trade</figcaption></figure>
$$ \text{trust}_{i,e,d,c} = \alpha_c + \beta \text{slave exports}_e + \mathbf{X}'_{i,e,d,c} \mathbf{\Gamma} + \mathbf{X}'_{d,c} \mathbf{\Omega} + \varepsilon_{i,e,d,c} $$

* trust$_{i,e,d,c}$: trust of individual i, ethnic group e, district d, country c.

* slave exports$_e$: number of slaves taken from ethnic group e (log normalized by land area).

* $\alpha_c$: country fixed effects.

* $\mathbf{X}'_{i,e,d,c}$: individual controls (age, gender, urban, living conditions, education, religion, occupation).

* $\mathbf{X}'_{d,c}$: ethnic controls for district (fractionalization, share same ethnicity).

* Two-way clustering on ethnic group and district.
OLS estimates of the determinants of the trust of others

<table><thead><tr><th></th><th>Trust of relatives (1)</th><th>Trust of neighbors (2)</th><th>Trust of local council (3)</th><th>Intra-group trust (4)</th><th>Inter-group trust (5)</th></tr></thead><tbody><tr><td>ln (1 + exports/area)</td><td>-0.133***<br>(0.037)</td><td>-0.159***<br>(0.034)</td><td>-0.111***<br>(0.021)</td><td>-0.144***<br>(0.032)</td><td>-0.097***<br>(0.028)</td></tr><tr><td>Individual controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>20,062</td><td>20,027</td><td>19,733</td><td>19,952</td><td>19,765</td></tr><tr><td>Number of ethnicity clusters</td><td>185</td><td>185</td><td>185</td><td>185</td><td>185</td></tr><tr><td>Number of district clusters</td><td>1,257</td><td>1,257</td><td>1,283</td><td>1,257</td><td>1,255</td></tr><tr><td>R<sup>2</sup></td><td>0.13</td><td>0.16</td><td>0.20</td><td>0.14</td><td>0.11</td></tr></tbody></table>
* S.d. of trust and slave measure close to 1.
  ⇒ Coefficients close to standardized.
* S.d. of trust and slave measure close to 1.
  ⇒ Coefficients close to standardized.

* Alternative: compare explanatory power with other variables.

* Standard variance decomposition.

* Slave exports and other covariates explain 5.4% of outcome.

* Slave exports explain 16–27% of this variation.
* Control for observables.

* Ethnicity-level determinants of colonial rule: disease environment and precolonial prosperity.

* Ethnicity-level characteristics of colonial rule: railway lines in 1911, European missionary contact.

* Same results.
* Control for observables.

* Ethnicity-level determinants of colonial rule: disease environment and precolonial prosperity.

* Ethnicity-level characteristics of colonial rule: railway lines in 1911, European missionary contact.

* Same results.

* Potential bias from unobservables.

* Altonji et al (2005): how much stronger selection on unobservables must be to explain away the full estimated effect.

* To explain main effects, selection on unobservables need to be four times greater on average.
* Control for observables.

* Ethnicity-level determinants of colonial rule: disease environment and precolonial prosperity.

* Ethnicity-level characteristics of colonial rule: railway lines in 1911, European missionary contact.

* Same results.

* Potential bias from unobservables.

* Altonji et al (2005): how much stronger selection on unobservables must be to explain away the full estimated effect.

* To explain main effects, selection on unobservables need to be four times greater on average.

* IV using historical distance from the coast with falsification tests.
OLS estimates of the determinants of the trust of others

<table><thead><tr><th></th><th>Trust of relatives (1)</th><th>Trust of neighbors (2)</th><th>Trust of local council (3)</th><th>Intragroup trust (4)</th><th>Intergroup trust (5)</th></tr></thead><tbody><tr><td>In (1 + exports/area)</td><td>-0.178***<br>(0.032)</td><td>-0.202***<br>(0.031)</td><td>-0.129***<br>(0.022)</td><td>-0.188***<br>(0.033)</td><td>-0.115***<br>(0.030)</td></tr><tr><td>Colonial population density</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ethnicity-level colonial controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>16,709</td><td>16,679</td><td>15,905</td><td>16,636</td><td>16,473</td></tr><tr><td>Number of ethnicity clusters</td><td>147</td><td>147</td><td>146</td><td>147</td><td>147</td></tr><tr><td>Number of district clusters</td><td>1,187</td><td>1,187</td><td>1,194</td><td>1,186</td><td>1,184</td></tr><tr><td>R<sup>2</sup></td><td>0.13</td><td>0.16</td><td>0.21</td><td>0.16</td><td>0.12</td></tr></tbody></table>
# Slave Trade and Contemporaneous Trust

## IV estimates of the effect of the slave trade on trust

<table><thead><tr><th></th><th>Trust of relatives (1)</th><th>Trust of neighbors (2)</th><th>Trust of local council (3)</th><th>Intragroup trust (4)</th><th>Intergroup trust (5)</th></tr></thead><tbody><tr><td colspan="6">Second stage: Dependent variable is an individual's trust</td></tr><tr><td>ln (1 + exports/area)</td><td>-0.190***<br>(0.067)</td><td>-0.245***<br>(0.070)</td><td>-0.221***<br>(0.060)</td><td>-0.251***<br>(0.088)</td><td>-0.174**<br>(0.080)</td></tr><tr><td>Hausman test (p-value)</td><td>0.88</td><td>0.53</td><td>0.09</td><td>0.44</td><td>0.41</td></tr><tr><td>R<sup>2</sup></td><td>0.13</td><td>0.16</td><td>0.20</td><td>0.15</td><td>0.12</td></tr><tr><td colspan="6">First stage: Dependent variable is ln (1 + exports/area)</td></tr><tr><td>Historical distance of ethnic group from coast</td><td>-0.0014***<br>(0.0003)</td><td>-0.0014***<br>(0.0003)</td><td>-0.0014***<br>(0.0003)</td><td>-0.0014***<br>(0.0003)</td><td>-0.0014***<br>(0.0003)</td></tr><tr><td>Colonial population density</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ethnicity-level colonial controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>District controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>16,709</td><td>16,679</td><td>15,905</td><td>16,636</td><td>16,473</td></tr><tr><td>Number of clusters</td><td>147 / 1,187</td><td>147 / 1,187</td><td>146 / 1,194</td><td>147 / 1,186</td><td>147 / 1,184</td></tr><tr><td>F-stat of excl. instrument</td><td>26.9</td><td>26.8</td><td>27.4</td><td>27.1</td><td>27.0</td></tr><tr><td>R<sup>2</sup></td><td>0.81</td><td>0.81</td><td>0.81</td><td>0.81</td><td>0.81</td></tr></tbody></table>
Falsification test: reduced form in Africa and Asia

<table><thead><tr><th rowspan="3"></th><th colspan="4">Trust of local government council</th></tr><tr><th colspan="2">Afrobarometer sample</th><th colspan="2">Asiabarometer sample</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Distance from the coast</td><td>0.00039***<br>(0.00009)</td><td>0.00031***<br>(0.00008)</td><td>-0.00001<br>(0.00010)</td><td>0.00001<br>(0.00009)</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Number of observations</td><td>19,913</td><td>19,913</td><td>5,409</td><td>5,409</td></tr><tr><td>Number of clusters</td><td>185</td><td>185</td><td>62</td><td>62</td></tr><tr><td>R<sup>2</sup></td><td>0.16</td><td>0.18</td><td>0.19</td><td>0.22</td></tr></tbody></table>
* Slave trade might have weakened institutions, generating mistrust.
* Slave trade might have weakened institutions, generating mistrust.

* Measure of this: mistrust in local government.

* Control for trust in local government.

* Use various measures through fixed effects.

* Half of the overall effect can be explained by effect through quality of local institutions.
* Slave trade might have weakened institutions, generating mistrust.

* Measure of this: mistrust in local government.

* Control for trust in local government.

* Use various measures through fixed effects.

* Half of the overall effect can be explained by effect through quality of local institutions.

* Effects through untrustworthiness of others.

* Measure of intergroup trust in local area: weighted average slave export intensity of other ethnic groups in same area.

* No effect from deteriorated intergroup trust.
## Identifying channels of causality

<table><thead><tr><th rowspan="2"></th><th colspan="4">Intergroup trust</th></tr><tr><th>Trust of local council<br>(1)</th><th>(2)</th><th>Within<br>town<br>(3)</th><th>Within<br>district<br>(4)</th><th>Within<br>province<br>(5)</th></tr></thead><tbody><tr><td>Ethnicity-based slave export measure<br>(baseline measure)</td><td>-0.072***<br>(0.019)</td><td>-0.070***<br>(0.019)</td><td>-0.102***<br>(0.028)</td><td>-0.120***<br>(0.027)</td><td>-0.098***<br>(0.029)</td></tr><tr><td>Average slave export measure among other<br>ethnicities in the same location</td><td></td><td></td><td>-0.037<br>(0.029)</td><td>-0.063**<br>(0.030)</td><td>-0.091***<br>(0.035)</td></tr><tr><td>Council trustworthiness fixed effects</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Five public goods fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Colonial population density</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ethnicity-level colonial controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Baseline controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>12,827</td><td>12,203</td><td>9,673</td><td>12,513</td><td>15,999</td></tr><tr><td>Number of clusters</td><td>146/1,172</td><td>145/1,130</td><td>147/725</td><td>147/737</td><td>147/1,127</td></tr><tr><td>R<sup>2</sup></td><td>0.37</td><td>0.37</td><td>0.12</td><td>0.12</td><td>0.12</td></tr></tbody></table>
* Effect of slave trade through external environment (institutions, trustworthiness of others) versus individuals’ internal norms.
* Effect of slave trade through external environment (institutions, trustworthiness of others) versus individuals' internal norms.

* Location-based measure slave intensity: slaves taken from area of residence.

* When both variables included, identification from movers (45% of sample).
* Effect of slave trade through external environment (institutions, trustworthiness of others) versus individuals' internal norms.

* Location-based measure slave intensity: slaves taken from area of residence.

* When both variables included, identification from movers (45% of sample).

* Ethnicity-based measures decrease by 10-15%, so explains 85-90% of the overall effect.

* It is always twice as large in magnitude than location-based effects.
## Identifying channels of causality

<table><thead><tr><th></th><th>Trust of relatives<br>(1)</th><th>Trust of neighbors<br>(2)</th><th>Trust of local council<br>(3)</th><th>Intragroup trust<br>(4)</th><th>Intergroup trust<br>(5)</th></tr></thead><tbody><tr><td>Ethnicity-based slave export measure<br>(baseline measure)</td><td>-0.155***<br>(0.029)</td><td>-0.182***<br>(0.029)</td><td>-0.100***<br>(0.023)</td><td>-0.169***<br>(0.033)</td><td>-0.090***<br>(0.030)</td></tr><tr><td>Location-based slave export measure</td><td>-0.045***<br>(0.014)</td><td>-0.045***<br>(0.016)</td><td>-0.045**<br>(0.018)</td><td>-0.043**<br>(0.018)</td><td>-0.047**<br>(0.020)</td></tr><tr><td>Colonial population density</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ethnicity-level colonial controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Baseline controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>15,999</td><td>15,972</td><td>15,221</td><td>15,931</td><td>15,773</td></tr><tr><td>Number of clusters</td><td>146/269</td><td>146/269</td><td>145/272</td><td>146/269</td><td>146/269</td></tr><tr><td>R<sup>2</sup></td><td>0.13</td><td>0.16</td><td>0.20</td><td>0.16</td><td>0.12</td></tr></tbody></table>
# THE INFLUENCE OF ANCESTRAL LIFEWAYS ON INDIVIDUAL ECONOMIC OUTCOMES IN SUB-SAHARAN AFRICA

**Stelios Michalopoulos**
Brown University

**David N. Weil**
Brown University

**Louis Putterman**
Brown University
* Place-based approach:

* Focuses on institutions and geography (Neolithic transition).

* More developed: easy to map space across time.

* Individual-based approach:

* Focuses on lineage, cultural transmission, intergenerational mobility.

* Less developed: little data on lineage.
* Does lineage to preindustrial subsistence matter for economic outcomes today in sub-Saharan Africa?

* Compare pastoralist vs agriculture ancestral subsistence lifeways.

* Focus on Africa because easy to match individuals to ethnic groups and ancestral lifeways.

* Distinguish between institution and cultural channels through within location variation.
The introduction of location fixed effects is crucial, since it allows us to absorb characteristics related to the geographic, ecological, and institutional environment of a given region that recent studies have highlighted as important determinants of regional African development. Moreover, it allows us to uncover the importance of portable ethnic-specific traits whose influence is not limited to the ancestral homeland of a given group. This methodology is similar to Nunn and Wantchekon (2011), who investigate the impact of slavery on individual trust among respondents residing outside their ethnic enclaves.
* Ethnicity data:

* DHS surveys.

* Information for both ethnicity and enumeration area coordinates.

* 337k respondents across 21 countries, 492 ethnicity-country groups.

* Matching modern ethnicities to ancestral groups:

* Murdock's Ethnographic Atlas.

* Match DHS ethnicities to those in atlas.

* Average distance moved: residence vs nearest border of ancestral homeland (51% movers).
* Modern outcomes (DHS):

* Education: categories and years completed.

* Wealth: quintiles.

* Historical mode of subsistence:

* Activities in Ethnographic Atlas: gathering, hunting, fishing, animal husbandry, agriculture.

* Shares of subsistence in 9 bands.

* In regressions: pastoralism (husbandry) is default activity, focal independent variable on degree of reliance on agriculture (the rest are controls).
<figure><img src="image_205.png" /></figure>
Predictive Power of Ethnicity

<table><thead><tr><th>R<sup>2</sup> from FE regressions</th><th>All education</th><th>All wealth</th><th>Movers education</th><th>Movers wealth</th></tr></thead><tbody><tr><td>Country FE</td><td>0.159</td><td>0.013</td><td>0.147</td><td>0.038</td></tr><tr><td>Homeland FE</td><td>0.291</td><td>0.231</td><td>0.290</td><td>0.295</td></tr><tr><td>Ethnicity FE</td><td>0.265</td><td>0.138</td><td>0.257</td><td>0.182</td></tr><tr><td>Country-ethnicity FE</td><td>0.281</td><td>0.159</td><td>0.282</td><td>0.209</td></tr><tr><td>Country-homeland FE</td><td>0.301</td><td>0.248</td><td>0.304</td><td>0.319</td></tr><tr><td>Country-homeland FE and country-ethnicity FE</td><td>0.325</td><td>0.283</td><td>0.337</td><td>0.362</td></tr><tr><td>Observations</td><td>285,255</td><td>285,263</td><td>154,744</td><td>154,747</td></tr></tbody></table>
$$y_{i,e,h,c,v} = \beta \text{Agriculture}_e + \delta \mathbf{X}_{i,e,h,c,v} + \alpha_c \cdot \alpha_{h,c} \cdot \alpha_v + \varepsilon_{i,e,h,c,v}$$

* $y_{i,e,h,c,v}$: outcome for individual i of ethnicity e residing in homeland h in country c and enumeration area v.

* $\text{Agriculture}_e$: subsistence share of agriculture of ancestral group e.

* $\mathbf{X}_{i,e,h,c,v}$: controls (age, female, mover status).

* $\alpha_c$: country FE.

* $\alpha_{h,c}$: country-homeland FE.

* $\alpha_v$: enumeration area FE.

* Clustering: ethnicity level.
## Benchmark: DHS regressions within ethnic homelands

<table><thead><tr><th>Variables</th><th>(1)<br>Education</th><th>(2)<br>Education</th><th>(3)<br>Education</th><th>(4)<br>Education</th><th>(5)<br>Wealth</th><th>(6)<br>Wealth</th><th>(7)<br>Wealth</th><th>(8)<br>Wealth</th></tr></thead><tbody><tr><td>Agriculture</td><td>0.2337***<br>(0.0432)</td><td>0.1498***<br>(0.0259)</td><td>0.1034***<br>(0.0212)</td><td>0.1011***<br>(0.0191)</td><td>0.2233***<br>(0.044)</td><td>0.1797***<br>(0.0324)</td><td>0.0970***<br>(0.0221)</td><td>0.0972***<br>(0.0219)</td></tr><tr><td>Gather/hunt/fish</td><td>0.2095***<br>(0.0547)</td><td>0.1026***<br>(0.0214)</td><td>0.0918***<br>(0.0200)</td><td>0.0911***<br>(0.0185)</td><td>0.1574***<br>(0.0457)</td><td>0.0681***<br>(0.0249)</td><td>0.0488***<br>(0.0152)</td><td>0.0524***<br>(0.0146)</td></tr><tr><td>Urban</td><td></td><td></td><td>0.9199***<br>(0.0357)</td><td>0.7079***<br>(0.0292)</td><td></td><td></td><td>1.6401***<br>(0.0460)</td><td>1.4330***<br>(0.0468)</td></tr><tr><td>Simple controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Country-ethnic homeland FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Occupation FE</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Observations</td><td>285,192</td><td>285,192</td><td>285,192</td><td>285,192</td><td>285,200</td><td>285,200</td><td>285,200</td><td>285,200</td></tr><tr><td>R<sup>2</sup></td><td>0.239</td><td>0.350</td><td>0.418</td><td>0.493</td><td>0.052</td><td>0.266</td><td>0.491</td><td>0.518</td></tr></tbody></table>

* Country characteristics only explain 1/3.

* Part explained by migration to urban locations.

* Differences in ancestral lifeways explain 1/3 of individual variation in outcomes due to ethnic identity.
# The Influence of Ancestral Characteristics

## Benchmark: DHS regressions within villages

<table><thead><tr><th>Variables</th><th>(1)<br>Education</th><th>(2)<br>Education</th><th>(3)<br>Education</th><th>(4)<br>Wealth</th><th>(5)<br>Wealth</th><th>(6)<br>Wealth</th></tr></thead><tbody><tr><td>Agriculture</td><td>0.1034***<br>(0.0212)</td><td>0.0731***<br>(0.0147)</td><td>0.0694***<br>(0.0128)</td><td>0.0970***<br>(0.0221)</td><td>0.0389***<br>(0.0070)</td><td>0.0379***<br>(0.0068)</td></tr><tr><td>Gather/hunt/fish</td><td>0.0918***<br>(0.0200)</td><td>0.0708***<br>(0.0155)</td><td>0.0681***<br>(0.0140)</td><td>0.0488***<br>(0.0152)</td><td>0.0176**<br>(0.0068)</td><td>0.0190***<br>(0.0065)</td></tr><tr><td>Urban</td><td>0.9199***<br>(0.0357)</td><td></td><td></td><td>1.6401***<br>(0.0460)</td><td></td><td></td></tr><tr><td>Simple controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-ethnic<br>Homeland FE</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Coordinates FE</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Occupation FE</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>285,192</td><td>285,192</td><td>285,192</td><td>285,200</td><td>285,200</td><td>285,200</td></tr><tr><td>R<sup>2</sup></td><td>0.418</td><td>0.506</td><td>0.559</td><td>0.491</td><td>0.677</td><td>0.682</td></tr></tbody></table>

* Shifting from pastoralism to agriculture raises education by 0.28 points, or 0.8 years of education.

* Shifting from pastoralism to agriculture raises wealth by 20% within
* Identification based on movers (54% of sample).

* Differential selection into migration across lifeway groups?

* Assess if ancestral lifeway predicts migration.

* Explicitly assess selective migration.
## Determinants of migration

<table><thead><tr><th>Variables</th><th>(1)<br>Mover</th><th>(2)<br>Mover</th><th>(3)<br>Moved in life</th><th>(4)<br>Moved in life</th></tr></thead><tbody><tr><td>Agriculture</td><td>-0.0465**<br>(0.0212)</td><td>-0.0464**<br>(0.0211)</td><td>0.0052<br>(0.0052)</td><td>0.0048<br>(0.0050)</td></tr><tr><td>Gather/hunt/fish</td><td>-0.1179***<br>(0.0254)</td><td>-0.1177***<br>(0.0253)</td><td>-0.0062<br>(0.0053)</td><td>-0.0061<br>(0.0051)</td></tr><tr><td>Simple controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Coordinates FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Occupation FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>285,200</td><td>285,200</td><td>188,304</td><td>188,304</td></tr><tr><td>R<sup>2</sup></td><td>0.768</td><td>0.768</td><td>0.206</td><td>0.210</td></tr></tbody></table>
# Selection into Migration

## Determinants of migration

<table><thead><tr><th>Variables</th><th>(1)<br>Education</th><th>(2)<br>Education</th><th>(3)<br>Wealth</th><th>(4)<br>Wealth</th></tr></thead><tbody><tr><td>Agriculture</td><td>0.2808***<br>(0.0607)</td><td>0.2710***<br>(0.0543)</td><td>0.2431***<br>(0.0559)</td><td>0.2368***<br>(0.0483)</td></tr><tr><td>Gather/hunt/fish</td><td>0.2816***<br>(0.0683)</td><td>0.2747***<br>(0.0638)</td><td>0.1826***<br>(0.0594)</td><td>0.1879***<br>(0.0499)</td></tr><tr><td>Mover</td><td>0.7459**<br>(0.3505)</td><td>0.7805**<br>(0.3280)</td><td>0.5330<br>(0.3417)</td><td>0.5994*<br>(0.3029)</td></tr><tr><td>Mover × agriculture</td><td>-0.0570<br>(0.0515)</td><td>-0.0731<br>(0.0482)</td><td>-0.0251<br>(0.0441)</td><td>-0.0462<br>(0.0393)</td></tr><tr><td>Mover × gather/hunt/fish</td><td>-0.1085**<br>(0.0482)</td><td>-0.1155***<br>(0.0441)</td><td>-0.0372<br>(0.0562)</td><td>-0.0529<br>(0.0472)</td></tr><tr><td>Simple controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Occupation FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>285,192</td><td>285,192</td><td>285,200</td><td>285,200</td></tr><tr><td>R<sup>2</sup></td><td>0.241</td><td>0.38</td><td>0.053</td><td>0.222</td></tr></tbody></table>
THE LONG-TERM EFFECT OF DEMOGRAPHIC SHOCKS ON THE EVOLUTION OF GENDER ROLES: EVIDENCE FROM THE TRANSATLANTIC SLAVE TRADE

Edoardo Teso

Harvard University and IQSS
* What are the mechanisms of persistence of historical shocks?

* The case of gender norms in Africa and the slave trade.
* What are the mechanisms of persistence of historical shocks?

* The case of gender norms in Africa and the slave trade.

* Alternative framing: explain current distribution in FLFP in Africa.
* What are the mechanisms of persistence of historical shocks?

* The case of gender norms in Africa and the slave trade.

* Alternative framing: explain current distribution in FLFP in Africa.

* Hypothesis:

* Male slaves outnumbered female slaves.

* This generated historically skewed sex ratios in Africa.

* Women had to substitute for men in previously male-dominated activities.

* This changed attitudes and beliefs in the long run, affecting FLFP.

* Multiple equilibria, intergenerational learning, institutional change.
## Demographic impact of the transatlantic slave trade

<figure><img src="image_206.png" /></figure>
* Contemporaneous data.

* FLFP data from DHS (61 surveys, 21 countries, 1992–2014).

* Sample of 662k women aged 15–49.

* Also information on occupation, fertility, age at first birth, attitudes, household tasks.
* Contemporaneous data.

* FLFP data from DHS (61 surveys, 21 countries, 1992–2014).

* Sample of 662k women aged 15–49.

* Also information on occupation, fertility, age at first birth, attitudes, household tasks.

* Historical data.

* Slaves taken from Nunn and Wantchekon (2011).

* Ethnicity data from Murdock's Atlas (1959).

* Matched with DHS: 583k women and 223k men.
<figure><img src="image_207.png" /><figcaption>Ethnic group-level exposure to transatlantic slave trade</figcaption></figure>
$$y_{i,e,c} = \alpha_c + \beta \text{Trades}_e + X'_{i,e,c} \Delta + Z'_e \Omega + \varepsilon_{i,e,c}$$

* $y_{i,e,c}$: outcome of woman $i$, in country $c$, ethnic group $e$.

* $\text{Trades}_e$: slaves taken in transatlantic or indian ocean slave trades, normalized by historical area.

* $X'_{i,e,c}$: individual controls (age, marital status, urban, religion).

* $Z'_e$: ethnicity controls (disease environment, precolonial urbanization, jurisdictional hierarchies, population density, colonial railroads, missions, agricultural practices, historical warfare).

* Clustering: ethnic group level.
OLS estimates, the effect of the slave trade on FLFP

<table><thead><tr><th></th><th>FLFP<br>(1)</th><th>FLFP<br>(2)</th><th>FLFP<br>(3)</th><th>FLFP<br>(4)</th><th>FLFP<br>(5)</th><th>FLFP<br>(6)</th></tr></thead><tbody><tr><td>Transatlantic Trade</td><td>0.048***<br>(0.013)</td><td>0.054***<br>(0.011)</td><td>0.059***<br>(0.013)</td><td>0.056***<br>(0.011)</td><td>0.073***<br>(0.012)</td><td>0.072***<br>(0.012)</td></tr><tr><td>Indian Ocean Trade</td><td>-0.059<br>(0.140)</td><td>-0.120<br>(0.158)</td><td>-0.061<br>(0.175)</td><td>-0.111<br>(0.174)</td><td>-0.146<br>(0.205)</td><td>-0.133<br>(0.196)</td></tr><tr><td>Observations</td><td>583,562</td><td>563,379</td><td>470,183</td><td>563,054</td><td>386,503</td><td>386,317</td></tr><tr><td>R-squared</td><td>0.16</td><td>0.17</td><td>0.18</td><td>0.18</td><td>0.14</td><td>0.14</td></tr><tr><td>Ethnic Groups</td><td>261</td><td>243</td><td>170</td><td>243</td><td>241</td><td>241</td></tr><tr><td>Country-survey FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Historical Controls</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Hist. Part. Agriculture</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Education</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Polygyny</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Transatlantic std. dev.</td><td>0.564</td><td>0.564</td><td>0.570</td><td>0.564</td><td>0.564</td><td>0.564</td></tr><tr><td>Indian Ocean std. dev.</td><td>0.033</td><td>0.031</td><td>0.034</td><td>0.031</td><td>0.031</td><td>0.031</td></tr><tr><td>Dep. var. mean unaffected</td><td>0.588</td><td>0.586</td><td>0.589</td><td>0.586</td><td>0.635</td><td>0.635</td></tr></tbody></table>
<figure><img src="image_208.png" /><figcaption>Residuals plot (Column 2)</figcaption></figure>
* ↑ 1 s.d. ancestor exposure to slave trade ⇒ ↑ 3pp FLFP.

* ↑ 1 s.d. ancestor exposure to slave trade ⇒ ↑ 5% FLFP mean.

* ↑ 1 s.d. ancestor exposure to slave trade ⇒ ↑ 0.06 FLFP s.d.
OLS estimates, the effect of the slave trade on occupational choices

<table><thead><tr><th></th><th>Agriculture<br>(1)</th><th>Clerical<br>(2)</th><th>Manual<br>(3)</th><th>Domestic<br>(4)</th><th>High Ranking<br>(5)</th></tr></thead><tbody><tr><td>Transatlantic Trade</td><td>0.018<br>(0.016)</td><td>-0.000<br>(0.001)</td><td>-0.010<br>(0.008)</td><td>-0.004**<br>(0.002)</td><td>0.048***<br>(0.011)</td></tr><tr><td>Observations</td><td>549,009</td><td>549,009</td><td>549,009</td><td>549,009</td><td>549,009</td></tr><tr><td>R-squared</td><td>0.23</td><td>0.02</td><td>0.05</td><td>0.07</td><td>0.14</td></tr><tr><td>Ethnic Groups</td><td>243</td><td>243</td><td>243</td><td>243</td><td>243</td></tr><tr><td>Country-survey FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Historical Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Transatlantic std. dev.</td><td>0.564</td><td>0.564</td><td>0.564</td><td>0.564</td><td>0.564</td></tr><tr><td>Dep. var. mean unaffected</td><td>0.276</td><td>0.011</td><td>0.061</td><td>0.026</td><td>0.224</td></tr></tbody></table>
* High ranking.

* ↑ 1 s.d. ancestor exposure ⇒ ↑ 3pp high ranking.

* ↑ 1 s.d. ancestor exposure ⇒ ↑ 12% high ranking mean.

* ↑ 1 s.d. ancestor exposure ⇒ ↑ 0.06 high ranking s.d.
* High ranking.

• ↑ 1 s.d. ancestor exposure ⇒ ↑ 3pp high ranking.

• ↑ 1 s.d. ancestor exposure ⇒ ↑ 12% high ranking mean.

• ↑ 1 s.d. ancestor exposure ⇒ ↑ 0.06 high ranking s.d.

* Domestic.

• ↑ 1 s.d. ancestor exposure ⇒ ↓ 0.2pp domestic.

• ↑ 1 s.d. ancestor exposure ⇒ ↓ 9% domestic mean.

• ↑ 1 s.d. ancestor exposure ⇒ ↓ 0.19 domestic s.d.

* But think of base rates.
# Male Falsification Test

## OLS estimates, women's versus men's employment

<table><thead><tr><th>Sample</th><th>Men<br>(1)</th><th>Women<br>(2)</th><th>Men<br>(3)</th><th>Women<br>(4)</th><th>Men<br>(5)</th><th>Women<br>(6)</th></tr></thead><tbody><tr><td>Transatlantic Trade</td><td>-0.010**<br>(0.005)</td><td>0.050***<br>(0.013)</td><td>-0.012**<br>(0.006)</td><td>0.054***<br>(0.010)</td><td>-0.008<br>(0.005)</td><td>0.056***<br>(0.010)</td></tr><tr><td>Observations</td><td>222,970</td><td>548,178</td><td>216,419</td><td>528,006</td><td>216,125</td><td>527,687</td></tr><tr><td>R-squared</td><td>0.31</td><td>0.16</td><td>0.31</td><td>0.17</td><td>0.32</td><td>0.18</td></tr><tr><td>Ethnic Groups</td><td>235</td><td>261</td><td>219</td><td>243</td><td>219</td><td>243</td></tr><tr><td>Country-survey FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Historical Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Education</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Transatlantic std. dev.</td><td>0.564</td><td>0.567</td><td>0.563</td><td>0.567</td><td>0.563</td><td>0.567</td></tr><tr><td>Dep. var. mean unaffected</td><td>0.831</td><td>0.593</td><td>0.831</td><td>0.591</td><td>0.831</td><td>0.591</td></tr></tbody></table>
* Women.

* ↑ 1 s.d. ancestor exposure ⇒ ↑ 3pp FLFP.

* ↑ 1 s.d. ancestor exposure ⇒ ↑ 5% FLFP mean.

* ↑ 1 s.d. ancestor exposure ⇒ ↑ 0.06 FLFP s.d.
• Women.

• ↑ 1 s.d. ancestor exposure ⇒ ↑ 3pp FLFP.

• ↑ 1 s.d. ancestor exposure ⇒ ↑ 5% FLFP mean.

• ↑ 1 s.d. ancestor exposure ⇒ ↑ 0.06 FLFP s.d.

• Men.

• ↑ 1 s.d. ancestor exposure ⇒ ↓ 0.7pp MLFP.

• ↑ 1 s.d. ancestor exposure ⇒ ↓ 0.8% MLFP mean.

• ↑ 1 s.d. ancestor exposure ⇒ ↓ 0.02 MLFP s.d.
* Hypothesis from Fernandez et al (2004):

* Working mothers transmit positive views about female labor to their sons.

* Women whose husbands' ancestors were more exposed to slave trade are more likely to have working mother, and thus working wife.
* Hypothesis from Fernandez et al (2004):

* Working mothers transmit positive views about female labor to their sons.

* Women whose husbands' ancestors were more exposed to slave trade are more likely to have working mother, and thus working wife.

* Use women's ancestry FE to isolate role of husband's origins.
* Hypothesis from Fernandez et al (2004):

* Working mothers transmit positive views about female labor to their sons.

* Women whose husbands' ancestors were more exposed to slave trade are more likely to have working mother, and thus working wife.

* Use women's ancestry FE to isolate role of husband's origins.

* To assess relative magnitude, use husband ancestry FE.
* Hypothesis from Fernandez et al (2004):

* Working mothers transmit positive views about female labor to their sons.

* Women whose husbands' ancestors were more exposed to slave trade are more likely to have working mother, and thus working wife.

* Use women's ancestry FE to isolate role of husband's origins.

* To assess relative magnitude, use husband ancestry FE.

* Cannot have causal interpretation:

* Husbands have an impact, holding beliefs constant.

* Selection into marriage with different types of men.
## OLS estimates, the marriage market channel

<table><thead><tr><th></th><th>FLFP<br>(1)</th><th>FLFP<br>(2)</th><th>FLFP<br>(3)</th></tr></thead><tbody><tr><td>Transatlantic Trade</td><td>0.071***<br>(0.015)</td><td></td><td>0.045***<br>(0.009)</td></tr><tr><td>Transatlantic Trade Husband</td><td></td><td>0.021**<br>(0.009)</td><td></td></tr><tr><td>Observations</td><td>109,310</td><td>109,294</td><td>109,293</td></tr><tr><td>R-squared</td><td>0.14</td><td>0.18</td><td>0.17</td></tr><tr><td>Ethnic Groups</td><td>232</td><td>228</td><td>232</td></tr><tr><td>Country-survey FE</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Country-survey-woman's ethnicity FE</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country-survey-husband's ethnicity FE</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Individual Controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Historical Controls</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Transatlantic std. dev.</td><td>0.558</td><td>0.559</td><td>0.558</td></tr><tr><td>Dep. var. mean unaffected</td><td>0.652</td><td>0.657</td><td>0.652</td></tr></tbody></table>
* Separate culture and institutions effects.

* Epidemiological approach using enumeration area FE.

* Find that half is due to cultural transmission.

* Not affected by heterogeneous response of movers relative to non-movers.
OLS estimates, the cultural transmission channel

<table><thead><tr><th></th><th>FLFP<br>(1)</th><th>FLFP<br>(2)</th><th>FLFP<br>(3)</th><th>FLFP<br>(4)</th><th>FLFP<br>(5)</th></tr></thead><tbody><tr><td>Transatlantic Trade</td><td>0.027***<br>(0.007)</td><td>0.029***<br>(0.005)</td><td>0.029***<br>(0.005)</td><td>0.036***<br>(0.006)</td><td>0.035***<br>(0.006)</td></tr><tr><td>Observations</td><td>583,377</td><td>563,092</td><td>562,766</td><td>386,121</td><td>385,935</td></tr><tr><td>R-squared</td><td>0.32</td><td>0.32</td><td>0.32</td><td>0.33</td><td>0.33</td></tr><tr><td>Ethnic Groups</td><td>261</td><td>243</td><td>243</td><td>241</td><td>241</td></tr><tr><td>EA-survey FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Historical Controls</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Education</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Polgyny</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Transatlantic std. dev.</td><td>0.564</td><td>0.564</td><td>0.564</td><td>0.564</td><td>0.564</td></tr><tr><td>Dep. var. mean unaffected</td><td>0.588</td><td>0.586</td><td>0.586</td><td>0.635</td><td>0.635</td></tr></tbody></table>
## OLS estimates, women's empowerment

<table><thead><tr><th></th><th>Share HH Decisions (1)</th><th>Share Violence (2)</th><th>Share Violence (3)</th><th>Rights Politics (4)</th><th>Rights Politics (5)</th><th>Rights General (6)</th><th>Rights General (7)</th></tr></thead><tbody><tr><td>Transatlantic Trade</td><td>0.048***<br>(0.013)</td><td>-0.008<br>(0.011)</td><td>-0.005<br>(0.009)</td><td>0.018<br>(0.031)</td><td>-0.057*<br>(0.031)</td><td>0.098***<br>(0.037)</td><td>-0.019<br>(0.057)</td></tr><tr><td>Observations</td><td>337,994</td><td>426,485</td><td>163,173</td><td>40,394</td><td>40,536</td><td>24,215</td><td>24,389</td></tr><tr><td>R-squared</td><td>0.26</td><td>0.22</td><td>0.11</td><td>0.07</td><td>0.07</td><td>0.10</td><td>0.09</td></tr><tr><td>Ethnic Groups</td><td>223</td><td>225</td><td>189</td><td>275</td><td>275</td><td>261</td><td>262</td></tr><tr><td>Sample</td><td>DHS</td><td>DHS</td><td>DHS</td><td>Afrob.</td><td>Afrob.</td><td>Afrob.</td><td>Afrob.</td></tr><tr><td>Gender</td><td>Female</td><td>Female</td><td>Male</td><td>Female</td><td>Male</td><td>Female</td><td>Male</td></tr><tr><td>Transatlantic std. dev.</td><td>0.567</td><td>0.570</td><td>0.564</td><td>0.536</td><td>0.536</td><td>0.540</td><td>0.538</td></tr><tr><td>Dep. var. mean unaffected</td><td>0.451</td><td>0.336</td><td>0.198</td><td>4.031</td><td>3.578</td><td>4.081</td><td>3.712</td></tr></tbody></table>
## Topic 6

# Political regimes, corruption, and development
Does good governance cause growth?
* Causal impact of democracy on growth.
  Acemoglu et al. (2019) Colagrossi et al. (2020)

* Causal impact of corruption on growth. Ugur (2014)
* Recall 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.
* 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.
* 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.
# Democracy 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>
* Relax assumption of constant impact over time:

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

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

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

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

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

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

<figure><img src="image_209.png" /></figure>
Time-varying relationship between gdp per capita growth and limited government

<figure><img src="image_210.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: $0.6\% \Rightarrow 1/4–2/3$ of growth.

* Long-lasting economic improvements from fiscal centralization.
* China, Singapore, Arab Springs. . . ⇒ is democracy irrelevant for growth since WWII?
* China, Singapore, Arab Springs. . . ⇒ is democracy irrelevant for growth since WWII?

* Widespread view, even from empirical studies.

* "More political rights do not have an effect on growth." Barro (1997)

* "The net effect of democracy on growth performance cross-nationally over the last five decades is negative or null." Gerring et al. (2005)
* China, Singapore, Arab Springs. . . ⇒ is democracy irrelevant for growth since WWII?

* Widespread view, even from empirical studies.

* "More political rights do not have an effect on growth." Barro (1997)

* "The net effect of democracy on growth performance cross-nationally over the last five decades is negative or null." Gerring et al. (2005)

* Acemoglu et al (2019) Explore causal relationship 1960-2010.

⇒ Transitioning to democracy ↑ 20% GDP per capita in 25 years.
* Measurement error in democratic transition.
* Measurement error in democratic transition.

* OVB: democracy is correlated with unobserved characteristics that affect growth.

⇒ use DiD (country FE)

* Democratizations are preceded by temporary dip in GDP.

⇒ Parallel-trends assumption is not valid.

* Time-varying unobservables related to future economic conditions
* Freedom House.

* Polity.

* Cheibub et al. (2010).

* Boix et al. (2012).
* 151 countries and 45 territories.

* Scores 0–4 for on 25 indicators.

* 10 political rights indicators.

* Electoral process (3).

* Political pluralism and participation (4).

* Functioning of government (3).

* 15 civil liberty indicators.

* Freedom of expression and belief (4).

* Associational and organizational rights (3).

* Rule of law (4).

* Personal autonomy and individual rights (4).

* 125 analysts and 40 advisers, yearly updates since 1972.
Global freedom status: 3 categories

<table><thead><tr><th rowspan="2">Status</th><th colspan="7">Political Rights score</th></tr><tr><th>0-5*</th><th>6-11</th><th>12-17</th><th>18-23</th><th>24-29</th><th>30-35</th><th>36-40</th></tr></thead><tbody><tr><td rowspan="7">Civil Liberties score</td><td>53-60</td><td>PF</td><td>PF</td><td>PF</td><td>F</td><td>F</td><td>F</td></tr><tr><td>44-52</td><td>PF</td><td>PF</td><td>PF</td><td>F</td><td>F</td><td>F</td></tr><tr><td>35-43</td><td>PF</td><td>PF</td><td>PF</td><td>PF</td><td>F</td><td>F</td></tr><tr><td>26-34</td><td>NF</td><td>PF</td><td>PF</td><td>PF</td><td>PF</td><td>F</td></tr><tr><td>17-25</td><td>NF</td><td>NF</td><td>PF</td><td>PF</td><td>PF</td><td>PF</td></tr><tr><td>8-16</td><td>NF</td><td>NF</td><td>NF</td><td>PF</td><td>PF</td><td>PF</td></tr><tr><td>0-7</td><td>NF</td><td>NF</td><td>NF</td><td>NF</td><td>PF</td><td>PF</td></tr></tbody></table>
Global freedom status: 3 categories

<figure><img src="image_211.png" /></figure>
* 167 countries 1800–2018

* 21-point scale from -10 to +10.

* POLITY = DEMOC - AUTOC.

* DEMOC: institutionalized democracy index (0–10).

* Competitiveness of executive recruitment (0–2)

* Openness of executive recruitment (0–1)

* Constraint on chief executive (0–4)

* Competitiveness of political participation (0–3)

* AUTOC: Authoritarian regime index (0–10). Same categories as above, different schemes.

* Coding by country experts, yearly updates since 1974.
Distribution of Annual POLITY Scores, 1800-2015

<figure><img src="image_212.png" /></figure>
<figure><img src="image_213.png" /></figure>
* Democracy-Dictatorship (DD): dichotomous measure of political regime.

* Based on 6-fold regime classification:

* Monarchy.

* Military dictatorship.

* Civilian dictatorship.

* Presidential democracy.

* Parliamentary democracy.

* Semi-presidential democracy.

* Relies on events rather than assessment (holding elections, existence than more than one party, changes in leadership of government).

* Criticizes false sense of continuous variation of Polity and FH.

* 199 countries since 1946.
Classifying forms of democratic government

<figure><img src="image_214.png" /></figure>
Classifying forms of dictatorial government

<figure><img src="image_215.png" /></figure>
* Democracy-Dictatorship: dichotomous measure of political regime.

* Democracy if meets following conditions:

* Contestation: executive directly or indirectly elected in popular elections; the legislature is chosen in free and fair elections.

* Participation: majority of adult men has right to vote.

* Relies on events rather than assessment (holding elections, existence than more than one party, changes in leadership of government).

* Extends Cheibub over time.

* 219 countries 1800–2007.
<figure><img src="image_216.png" /></figure>
<table><thead><tr><th>Variable</th><th>BMR</th><th>CGV</th><th>Polity ≥ 5</th><th>FH ≤ 3.5</th><th>Golder</th><th>HT</th><th>Average</th></tr></thead><tbody><tr><td>BMR</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td>.942</td></tr><tr><td>CGV</td><td>.957</td><td>1.000</td><td></td><td></td><td></td><td></td><td>.924</td></tr><tr><td>Polity ≥ 5</td><td>.939</td><td>.921</td><td>1.000</td><td></td><td></td><td></td><td>.928</td></tr><tr><td>FH ≤ 3.5</td><td>.934</td><td>.902</td><td>.933</td><td>1.000</td><td></td><td></td><td>.921</td></tr><tr><td>Golder</td><td>.958</td><td>.950</td><td>.924</td><td>.913</td><td>1.000</td><td></td><td>.931</td></tr><tr><td>HT</td><td>.921</td><td>.891</td><td>.921</td><td>.924</td><td>.910</td><td>1.000</td><td>.913</td></tr></tbody></table>
<figure><img src="image_217.png" /></figure>
* Annual panel of 175 countries, 1960–2010.

* Binary measure of democracy Papaioannou and Siourounis (2008)

$D_{ct} = 1$ if both

* Freedom House: “free” or “partially free.”

* Polity IV: positive score.
* Annual panel of 175 countries, 1960–2010.

* Binary measure of democracy Papaioannou and Siourounis (2008)

$D_{ct} = 1$ if both

* Freedom House: "free" or "partially free."

* Polity IV: positive score.

* Verify with Cheibub et al (2010) and Boix et al (2012)

* Countries that are democracies: 31.5% in 1960, 64.1% in 2010.

* 122 democratizations and 71 reversals.

* Code permanent and transitory changes in political regime.
<figure><img src="image_218.png" /><figcaption>GDP per Capita Before and After a Democratization</figcaption></figure>
Data: GDP per Capita and Economic Variables

* Log GDP per capita in 2000 US dollars. WB

* Investment, trade, education, infant mortality. WB

* Financial flows, tax revenues, economic reforms index, social unrests.
$$y_{ct} = \beta D_{ct} + \sum_{j=1}^{p} \gamma_j y_{ct-j} + \alpha_c + \delta_t + \varepsilon_{ct}$$

* $y_{ct}$: log GDP per capita in country c at time t.

* $D_{ct}$: dichotomous measure of democracy.

* $\alpha_c$: country fixed effects.

* $\delta_t$: year fixed effects.

* $p$: lags (control for dynamics).
$$ \mathbb{E}(\varepsilon_{ct} | y_{ct-1}, \dots, y_{ct_0}, D_{ct}, \dots, D_{ct_0}, \alpha_c, \delta_t) = 0 $$

* Democracy and past GDP are orthogonal to contemporaneous and future shocks to GDP.

* Countries that transition are not on a different GDP trend relative to others with similar levels of GDP in the past (lags) and level of long-run development (country FE).
$$ \mathbb{E}(\varepsilon_{ct} | y_{ct-1}, \dots, y_{ct_0}, D_{ct}, \dots, D_{ct_0}, \alpha_c, \delta_t) = 0 $$

* Democracy and past GDP are orthogonal to contemporaneous and future shocks to GDP.

* Countries that transition are not on a different GDP trend relative to others with similar levels of GDP in the past (lags) and level of long-run development (country FE).

* Lags of GDP summarizes factors that affect both *D* and *y*.

* Many of these impact future *y* through their impact on current *y*.

⇒ Lags + country FE control for selection into democracy.
Effect of Democracy on Log GDP per Capita

<table><thead><tr><th></th><th colspan="4">WITHIN ESTIMATES</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Democracy</td><td>0.973<br>(0.294)</td><td>0.651<br>(0.248)</td><td>0.787<br>(0.226)</td><td>0.887<br>(0.245)</td></tr><tr><td>log GDP first lag</td><td>0.973<br>(0.006)</td><td>1.266<br>(0.038)</td><td>1.238<br>(0.038)</td><td>1.233<br>(0.039)</td></tr><tr><td>log GDP second lag</td><td></td><td>-0.300<br>(0.037)</td><td>-0.207<br>(0.046)</td><td>-0.214<br>(0.043)</td></tr><tr><td>log GDP third lag</td><td></td><td></td><td>-0.026<br>(0.028)</td><td>-0.021<br>(0.028)</td></tr><tr><td>log GDP fourth lag</td><td></td><td></td><td>-0.043<br>(0.017)</td><td>-0.039<br>(0.034)</td></tr><tr><td>p-value lags 5 to 8</td><td></td><td></td><td></td><td>[ 0.565 ]</td></tr><tr><td>Long-run effect of democracy</td><td>35.587<br>(13.998)</td><td>19.599<br>(8.595)</td><td>21.240<br>(7.215)</td><td>22.008<br>(7.740)</td></tr><tr><td>Effect of democracy after 25 years</td><td>17.791<br>(5.649)</td><td>13.800<br>(5.550)</td><td>16.895<br>(5.297)</td><td>17.715<br>(5.455)</td></tr><tr><td>Persistence of GDP process</td><td>0.973<br>(0.006)</td><td>0.967<br>(0.005)</td><td>0.963<br>(0.005)</td><td>0.960<br>(0.007)</td></tr><tr><td>AR2 test p-value</td><td></td><td></td><td></td><td></td></tr><tr><td>Unit root test t-statistics</td><td>-4.79</td><td>-3.89</td><td>-4.13</td><td>-7.00</td></tr><tr><td>p-value (reject unit root)</td><td>[0.00]</td><td>[0.00]</td><td>[0.00]</td><td>[0.00]</td></tr><tr><td>Observations</td><td>6,790</td><td>6,642</td><td>6,336</td><td>5,688</td></tr><tr><td>Countries in sample</td><td>175</td><td>175</td><td>175</td><td>175</td></tr></tbody></table>
* First lag close to 1: large persistence in GDP.

* Short-run effect of democracy: ↑ 1% per year.

* Cumulative long-run effect of permanent transition:

$$ \frac{\hat{\beta}}{1 - \sum_{j=1}^{P} \hat{\gamma}_{j}} $$

* ↑ 36% over the long-run.

* ↑ 18% over 25 years.

* Including 4 lags: smaller effects, but sizable (preferred specification).
<figure><img src="image_219.png" /><figcaption>Effect of Democracy on Log GDP per Capita over Time</figcaption></figure>
* Main threat: time-varying factors that simultaneously affect democracy and GDP.

* Country FE absorb time-invariant factors.

* Solutions:

* Interact indicators for quantile of GDP per capita in 1960 with year FE.

* Interact indicators for Soviet transition with year FE.

* Control for 4 lags of unrest, trade exposure, financial flows, population.
* Main threat: time-varying factors that simultaneously affect democracy and GDP.

* Country FE absorb time-invariant factors.

* Solutions:

* Interact indicators for quantile of GDP per capita in 1960 with year FE.

* Interact indicators for Soviet transition with year FE.

* Control for 4 lags of unrest, trade exposure, financial flows, population.

* Robust to alternative measures of democracy and GDP.

* Not sensitive to outliers.
Effect of Democracy on Log GDP per Capita

<table><thead><tr><th rowspan="2">COVARIATES INCLUDED:</th><th colspan="8">Panel A: Within estimates.</th></tr><tr><th>(1)</th><th>GDP in 1960<br>quintiles ×<br>year effects</th><th>Soviet<br>dummies</th><th>Lags of<br>unrest</th><th>Lags of<br>trade</th><th>Lags of<br>financial<br>flows</th><th>Lags of<br>demographic<br>structure</th><th>Region ×<br>regime ×<br>year effects</th></tr></thead><tbody><tr><td>Democracy</td><td>0.787<br>(0.226)</td><td>0.718<br>(0.249)</td><td>0.911<br>(0.251)</td><td>0.705<br>(0.224)</td><td>0.595<br>(0.264)</td><td>0.926<br>(0.244)</td><td>0.650<br>(0.230)</td><td>0.834<br>(0.264)</td></tr><tr><td>Long-run effect of democracy</td><td>21.240<br>(7.215)</td><td>22.173<br>(8.702)</td><td>24.860<br>(7.783)</td><td>17.000<br>(5.980)</td><td>14.593<br>(7.122)</td><td>23.870<br>(8.211)</td><td>14.153<br>(5.419)</td><td>16.651<br>(5.546)</td></tr><tr><td>Effect of democracy after 25 years</td><td>16.895<br>(5.297)</td><td>16.261<br>(5.982)</td><td>19.587<br>(5.724)</td><td>13.567<br>(4.644)</td><td>11.509<br>(5.336)</td><td>18.149<br>(5.435)</td><td>12.251<br>(4.552)</td><td>14.532<br>(4.726)</td></tr><tr><td>Persistence of GDP process</td><td>0.963<br>(0.005)</td><td>0.968<br>(0.005)</td><td>0.963<br>(0.005)</td><td>0.959<br>(0.004)</td><td>0.959<br>(0.006)</td><td>0.961<br>(0.006)</td><td>0.954<br>(0.005)</td><td>0.950<br>(0.005)</td></tr><tr><td>Observations</td><td>6336</td><td>5,523</td><td>6,336</td><td>5,643</td><td>5,750</td><td>4,950</td><td>6,262</td><td>6,336</td></tr><tr><td>Countries in sample</td><td>175</td><td>149</td><td>175</td><td>171</td><td>172</td><td>171</td><td>172</td><td>175</td></tr></tbody></table>
IV Estimates: Democratization Waves

* Democratizations often occur in regional waves:

* Latin America and the Caribbean: 1980s and early 1990s.

* Soviet Union (Eastern Europe, Central Asia): 1990s.
# IV Estimates: Democratization Waves

* Democratizations often occur in regional waves:

* Latin America and the Caribbean: 1980s and early 1990s.

* Soviet Union (Eastern Europe, Central Asia): 1990s.

* Seven regions: Africa, East Asia and the Pacific, Eastern Europe and Central Asia, Western Europe and developed countries, Latin America and the Caribbean, MENA, South Asia.
# IV Estimates: Democratization Waves

<figure><img src="image_220.png" /><figcaption>Regional Democratizations Waves</figcaption></figure>
IV Estimates: Democratization Waves

• $D_{ct_0}$: country is democracy or not at start of sample.

• $R_c$: geographic region.

• $I_c = \{c' : c' \neq c, R_{c'} = R_c, D_{c't_0} = D_{ct_0}\}$: countries of same region that share similar political history.
# IV Estimates: Democratization Waves

* $D_{ct_0}$: country is democracy or not at start of sample.

* $R_c$: geographic region.

* $I_c = \{c' : c' \neq c, R_{c'} = R_c, D_{c't_0} = D_{ct_0}\}$: countries of same region that share similar political history.

* Instrument: $Z_{ct} = \frac{1}{|I_c|} \sum_{c' \in I_c} D_{c't}$.

* Average democracy in a region, excluding own-country observation.

* Instrument democracy with $Z_{ct}$ and its lags.
# IV Estimates: Democratization Waves

## IV Estimates of the Effect of Democracy on GDP per Capita

<table><thead><tr><th colspan="2">COVARIATES INCLUDED:</th><th>GDP in 1960<br>quintiles×<br>year effects</th><th>Soviet<br>dummies</th><th>Regional<br>trends</th><th>Regional<br>GDP &amp;<br>trade</th><th>Regional<br>unrest<br>GDP &amp; trade</th><th>Spatial<br>lag of GDP</th><th>Spatial lags<br>of GDP and<br>democracy</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td colspan="10" style="text-align: center; font-weight: bold;">Panel A: 2SLS estimates with fixed effects.</td></tr><tr><td>Democracy</td><td>0.966<br>(0.558)</td><td>1.149<br>(0.554)</td><td>1.125<br>(0.689)</td><td>1.292<br>(0.651)</td><td>1.697<br>(0.885)</td><td>1.817<br>(0.663)</td><td>1.107<br>(0.656)</td><td>1.335<br>(0.536)</td><td>1.361<br>(0.895)</td></tr><tr><td>Long-run effect of democracy</td><td>26.315<br>(17.075)</td><td>31.521<br>(17.425)</td><td>35.226<br>(23.846)</td><td>35.723<br>(19.997)</td><td>36.788<br>(20.657)</td><td>41.544<br>(17.157)</td><td>25.016<br>(16.002)</td><td>37.482<br>(17.836)</td><td>38.439<br>(27.883)</td></tr><tr><td>Effect of democracy after 25 years</td><td>20.836<br>(12.862)</td><td>24.866<br>(12.978)</td><td>25.618<br>(16.538)</td><td>27.929<br>(14.944)</td><td>32.051<br>(17.703)</td><td>35.350<br>(14.017)</td><td>21.386<br>(13.342)</td><td>29.217<br>(12.894)</td><td>29.011<br>(19.092)</td></tr><tr><td>Persistence of GDP process</td><td>0.963<br>(0.005)</td><td>0.964<br>(0.005)</td><td>0.968<br>(0.005)</td><td>0.964<br>(0.005)</td><td>0.954<br>(0.006)</td><td>0.956<br>(0.006)</td><td>0.956<br>(0.006)</td><td>0.964<br>(0.005)</td><td>0.965<br>(0.006)</td></tr><tr><td>Hansen p-value</td><td></td><td>0.211</td><td>0.181</td><td>0.322</td><td>0.28</td><td>0.25</td><td>0.09</td><td>0.04</td><td>0.19</td></tr><tr><td>Observations</td><td>6,312</td><td>6,309</td><td>5,496</td><td>6,309</td><td>6,309</td><td>6,309</td><td>6,309</td><td>6,181</td><td>6,009</td></tr><tr><td>Countries in sample</td><td>174</td><td>174</td><td>148</td><td>174</td><td>174</td><td>174</td><td>174</td><td>173</td><td>173</td></tr><tr><td>Ex. Instruments F-stat.</td><td>119.1</td><td>33.2</td><td>16.8</td><td>26.7</td><td>23.7</td><td>13.6</td><td>16.7</td><td>17.5</td><td>4.6</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">Panel B: First-stage estimates.</td></tr><tr><td>Democracy wave t-1</td><td>0.800<br>(0.073)</td><td>0.547<br>(0.101)</td><td>0.503<br>(0.139)</td><td>0.480<br>(0.099)</td><td>0.498<br>(0.092)</td><td>0.522<br>(0.104)</td><td>0.508<br>(0.102)</td><td>0.540<br>(0.103)</td><td>0.586<br>(0.101)</td></tr><tr><td>Democracy wave t-2</td><td></td><td>0.133</td><td>0.109</td><td>0.133</td><td>0.129</td><td>0.117</td><td>0.115</td><td>0.136</td><td>0.128</td></tr><tr><td>Democracy wave t-3</td><td></td><td>(0.081)</td><td>(0.094)</td><td>(0.080)</td><td>(0.081)</td><td>(0.079)</td><td>(0.078)</td><td>(0.078)</td><td>(0.088)</td></tr><tr><td>Democracy wave t-4</td><td></td><td>0.227<br>(0.067)</td><td>0.270<br>(0.077)</td><td>0.223<br>(0.065)</td><td>0.228<br>(0.079)</td><td>0.221<br>(0.069)</td><td>0.224<br>(0.070)</td><td>0.224<br>(0.070)</td><td>0.282<br>(0.077)</td></tr><tr><td></td><td></td><td>-0.087<br>(0.110)</td><td>-0.119<br>(0.126)</td><td>-0.075<br>(0.110)</td><td>-0.123<br>(0.106)</td><td>-0.083<br>(0.113)</td><td>-0.064<br>(0.113)</td><td>-0.072<br>(0.113)</td><td>-0.107<br>(0.116)</td></tr></tbody></table>
* Potential hypotheses for intervening variables:

* Investment

* TFP

* Economic reforms

* Taxes

* Schooling

* Child mortality

* Social unrest
* Potential hypotheses for intervening variables:

* Investment

* TFP

* Economic reforms

* Taxes

* Schooling

* Child mortality

* Social unrest

* Specification

$$m_{ct} = \beta D_{ct} + \sum_{j=1}^{p} \gamma_j y_{ct-j} + \sum_{j=1}^{p} \eta_j m_{ct-j} + \alpha_c + \delta_t + \varepsilon_{ct}$$
## Effects of Democracy on Potential Mechanisms

<table><thead><tr><th>DEPENDENT VARIABLE:</th><th>Log of investment share in GDP<br>(1)</th><th>Log of TFP<br>(2)</th><th>Index of economic reforms<br>(3)</th><th>Log of trade share in GDP<br>(4)</th><th>Log of tax share in GDP<br>(5)</th><th>Log of primary enrollment<br>(6)</th><th>Log of secondary enrollment<br>(7)</th><th>log of child mortality<br>(8)</th><th>Dummy for unrest<br>(9)</th></tr></thead><tbody><tr><td colspan="10" style="text-align: center; font-weight: bold;">Panel A: Within estimates.</td></tr><tr><td>Democracy</td><td>2.391<br>(1.114)</td><td>-0.205<br>(0.276)</td><td>0.687<br>(0.348)</td><td>0.689<br>(0.676)</td><td>3.311<br>(1.409)</td><td>1.042<br>(0.338)</td><td>1.345<br>(0.610)</td><td>-0.253<br>(0.063)</td><td>-7.832<br>(2.185)</td></tr><tr><td>Long-run effect of democracy</td><td>9.112<br>(4.255)</td><td>-2.883<br>(3.858)</td><td>5.580<br>(2.883)</td><td>5.445<br>(5.253)</td><td>16.062<br>(6.650)</td><td>21.908<br>(7.624)</td><td>18.960<br>(8.622)</td><td>-34.264<br>(19.747)</td><td>-11.944<br>(3.329)</td></tr><tr><td>Effect of democracy after 25 years</td><td>9.089<br>(4.245)</td><td>-2.738<br>(3.648)</td><td>5.359<br>(2.753)</td><td>5.303<br>(5.126)</td><td>15.864<br>(6.754)</td><td>18.892<br>(6.974)</td><td>18.057<br>(8.146)</td><td>-21.400<br>(5.124)</td><td>-11.944<br>(3.329)</td></tr><tr><td>Persistence of outcome process</td><td>0.738<br>(0.029)</td><td>0.929<br>(0.012)</td><td>0.877<br>(0.012)</td><td>0.873<br>(0.011)</td><td>0.794<br>(0.016)</td><td>0.952<br>(0.008)</td><td>0.929<br>(0.013)</td><td>0.993<br>(0.001)</td><td>0.344<br>(0.030)</td></tr><tr><td>Observations</td><td>5.665</td><td>3.879</td><td>4.692</td><td>5.738</td><td>4.511</td><td>3.714</td><td>2.883</td><td>6.084</td><td>5.646</td></tr><tr><td>Countries in sample</td><td>169</td><td>107</td><td>150</td><td>172</td><td>131</td><td>166</td><td>158</td><td>173</td><td>171</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">Panel B: 2SLS estimates.</td></tr><tr><td>Democracy</td><td>2.211<br>(2.852)</td><td>-0.941<br>(0.667)</td><td>3.224<br>(0.963)</td><td>5.512<br>(2.065)</td><td>8.088<br>(3.021)</td><td>1.757<br>(0.721)</td><td>4.116<br>(1.626)</td><td>-0.715<br>(0.164)</td><td>-5.569<br>(5.682)</td></tr><tr><td>Long-run effect of democracy</td><td>8.440<br>(10.705)</td><td>-12.728<br>(8.854)</td><td>23.775<br>(6.215)</td><td>10.589<br>(13.589)</td><td>36.609<br>(14.330)</td><td>36.691<br>(15.505)</td><td>57.072<br>(21.698)</td><td>-95.728<br>(26.347)</td><td>-8.471<br>(8.577)</td></tr><tr><td>Effect of democracy after 25 years</td><td>8.419<br>(10.681)</td><td>-12.167<br>(8.380)</td><td>23.156<br>(6.039)</td><td>39.817<br>(13.575)</td><td>38.159<br>(14.121)</td><td>31.611<br>(12.963)</td><td>54.252<br>(20.267)</td><td>-58.625<br>(13.123)</td><td>-8.471<br>(8.577)</td></tr><tr><td>Persistence of outcome process</td><td>0.738<br>(0.029)</td><td>0.926<br>(0.012)</td><td>0.864<br>(0.012)</td><td>0.864<br>(0.012)</td><td>0.791<br>(0.017)</td><td>0.952<br>(0.008)</td><td>0.928<br>(0.013)</td><td>0.993<br>(0.001)</td><td>0.343<br>(0.030)</td></tr><tr><td>Exc. instruments F-stat.</td><td>21.7</td><td>27.7</td><td>43.7</td><td>21.5</td><td>31.8</td><td>12.1</td><td>10.4</td><td>26.3</td><td>28.6</td></tr><tr><td>Hansen p-value</td><td>0.029</td><td>0.066</td><td>0.022</td><td>0.069</td><td>0.069</td><td>0.069</td><td>0.012</td><td>0.029</td><td>0.084</td></tr><tr><td>Observations</td><td>5.640</td><td>3.871</td><td>4.670</td><td>5.714</td><td>4.409</td><td>3.710</td><td>2.879</td><td>6.057</td><td>5.619</td></tr><tr><td>Countries in sample</td><td>168</td><td>107</td><td>149</td><td>171</td><td>130</td><td>164</td><td>156</td><td>172</td><td>170</td></tr></tbody></table>
# Does Democracy Need Development?

* Typical argument: democracy is good but only at high level of development and human capital.
* Typical argument: democracy is good but only at high level of development and human capital.

* Implementation: interactions between democracy and levels of development and human capital.

* If theory is valid, expect:

* Positive interaction terms.

* Main effect of democracy for low-countries to be negative
# Does Democracy Need Development?

## Heterogeneous Effects of Democracy on GDP per Capita

<table><thead><tr><th rowspan="2">INTERACTION WITH:<br>MEASURED AT:</th><th colspan="4">Log GDP per capita:</th><th colspan="4">Share with secondary:</th></tr><tr><th>1960<br>(1)</th><th>1970<br>(2)</th><th>1980<br>(3)</th><th>Lagged<br>(4)</th><th>1960<br>(5)</th><th>1970<br>(6)</th><th>1980<br>(7)</th><th>Lagged<br>(8)</th></tr></thead><tbody><tr><td colspan="9" style="text-align: center; font-weight: bold;">Panel A: Within estimates.</td></tr><tr><td>Democracy</td><td>0.432<br>(0.275)</td><td>0.572<br>(0.248)</td><td>0.687<br>(0.248)</td><td>0.744<br>(0.246)</td><td>0.446<br>(0.254)</td><td>0.340<br>(0.253)</td><td>0.385<br>(0.246)</td><td>0.495<br>(0.241)</td></tr><tr><td>Interaction</td><td>0.001<br>(0.002)</td><td>0.001<br>(0.001)</td><td>0.002<br>(0.002)</td><td>0.001<br>(0.002)</td><td>0.046<br>(0.028)</td><td>0.049<br>(0.020)</td><td>0.038<br>(0.014)</td><td>0.020<br>(0.013)</td></tr><tr><td>Long-run effect of democracy</td><td>16.231<br>(11.160)</td><td>18.631<br>(9.073)</td><td>20.489<br>(8.608)</td><td>19.843<br>(8.255)</td><td>13.785<br>(8.550)</td><td>10.480<br>(8.275)</td><td>11.841<br>(8.118)</td><td>14.597<br>(8.432)</td></tr><tr><td>Effect of democracy after 25 years</td><td>10.013<br>(6.556)</td><td>12.916<br>(5.960)</td><td>14.985<br>(5.848)</td><td>15.877<br>(5.943)</td><td>10.081<br>(5.964)</td><td>7.679<br>(5.872)</td><td>8.687<br>(5.728)</td><td>10.953<br>(5.821)</td></tr><tr><td>Persistence of GDP process</td><td>0.973<br>(0.005)</td><td>0.969<br>(0.005)</td><td>0.966<br>(0.005)</td><td>0.963<br>(0.006)</td><td>0.968<br>(0.005)</td><td>0.967<br>(0.005)</td><td>0.967<br>(0.005)</td><td>0.966<br>(0.006)</td></tr><tr><td>Observations</td><td>4,281</td><td>4,909</td><td>5,525</td><td>6,336</td><td>5,300</td><td>5,300</td><td>5,300</td><td>5,300</td></tr><tr><td>Countries in sample</td><td>93</td><td>109</td><td>131</td><td>175</td><td>138</td><td>138</td><td>138</td><td>138</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Panel B: SLS estimates.</td></tr><tr><td>Democracy</td><td>0.500<br>(1.088)</td><td>0.155<br>(0.961)</td><td>0.645<br>(0.929)</td><td>1.326<br>(0.887)</td><td>-0.119<br>(0.662)</td><td>-0.484<br>(0.665)</td><td>-0.474<br>(0.639)</td><td>0.600<br>(0.576)</td></tr><tr><td>Interaction</td><td>-0.002<br>(0.005)</td><td>0.000<br>(0.004)</td><td>-0.000<br>(0.004)</td><td>-0.003<br>(0.004)</td><td>0.174<br>(0.060)</td><td>0.156<br>(0.047)</td><td>0.116<br>(0.033)</td><td>0.049<br>(0.023)</td></tr><tr><td>Long-run effect of democracy</td><td>18.838<br>(43.554)</td><td>4.578<br>(31.473)</td><td>19.275<br>(30.208)</td><td>36.116<br>(29.900)</td><td>14.586<br>(19.968)</td><td>-14.586<br>(19.023)</td><td>-14.135<br>(18.114)</td><td>13.737<br>(18.629)</td></tr><tr><td>Effect of democracy after 25 years</td><td>11.592<br>(25.874)</td><td>3.486<br>(21.795)</td><td>14.078<br>(21.085)</td><td>28.377<br>(21.317)</td><td>-2.692<br>(14.837)</td><td>-10.843<br>(14.524)</td><td>-10.574<br>(13.901)</td><td>13.133<br>(13.312)</td></tr><tr><td>Persistence of GDP process</td><td>0.973<br>(0.006)</td><td>0.969<br>(0.006)</td><td>0.967<br>(0.006)</td><td>0.963<br>(0.008)</td><td>0.967<br>(0.006)</td><td>0.967<br>(0.006)</td><td>0.966<br>(0.006)</td><td>0.965<br>(0.006)</td></tr><tr><td>Exc. instruments F-stat.</td><td>6.6<br>(0.81)</td><td>6.1<br>(0.73)</td><td>7.0<br>(0.54)</td><td>14.0<br>(0.33)</td><td>18.5<br>(0.44)</td><td>17.6<br>(0.41)</td><td>16.0<br>(0.25)</td><td>12.4<br>(0.50)</td></tr><tr><td>Hansen p-value</td><td>0.423<br>(4.273)</td><td>4.901<br>(109)</td><td>5.517<br>(131)</td><td>6.153<br>(174)</td><td>5.292<br>(138)</td><td>5.292<br>(138)</td><td>5.292<br>(138)</td><td>5.218<br>(138)</td></tr><tr><td>Countries in sample</td><td>93</td><td>109</td><td>131</td><td>174</td><td>138</td><td>138</td><td>138</td><td>138</td></tr></tbody></table>

Long-run effects evaluated at the 25th percentile of interacted variable.
Colagrossi et al. (2020)

* Democracy and growth: one of the most researched topic.

* Each study is one piece of evidence (even Acemloglu's).

* Meta-analysis helps summarize findings quantitatively.

* Also test main hypothesis: human capital.
* Search "democracy AND growth" in academic databases (Econlit, Jstor, RePEc, ScienceDirect, SSRN).

* Literature cited in 9 qualitative literature reviews.

* Literature cited and quoting the 4 most cited papers.

* Literature used in previous meta-analysis.

* Doucouliagos and Ulubasoglu (2008).
* Included if coefficient and standard error (also on HK).

* Include working papers and unpublished studies.

* Collect all estimates from main specifications and robustness.

* Time range: 1983–2019.
* Included if coefficient and standard error (also on HK).

* Include working papers and unpublished studies.

* Collect all estimates from main specifications and robustness.

* Time range: 1983–2019.

* Sample:

* 2,047 $\beta_{dem}$ in 188 studies (1,227 main estimates).

* 875 $\beta_{hc}$ in 111 studies.

* 764 $\beta_{dem|hc}$ in 111 studies.

* Standardize coefficients (use partial correlations and Fisher transformation).
Democracy, human capital, and economic growth

<figure><img src="image_221.png" /><figcaption>Democracy and economic growth</figcaption></figure>
<figure><img src="image_222.png" /><figcaption>Human capital and economic growth</figcaption></figure>
Democracy, human capital, and economic growth

<figure><img src="image_223.png" /></figure>
* Start with a dataset of *K* estimates across *N* studies, along with their standard errors.

* Each within-study estimate is standardized for comparability: divide coefficient by s.d. of outcome and multiply by s.d. of treatment.

* Here: Fisher-z (alternative way of standardizing estimates).
* Start with a dataset of *K* estimates across *N* studies, along with their standard errors.

* Each within-study estimate is standardized for comparability: divide coefficient by s.d. of outcome and multiply by s.d. of treatment.

* Here: Fisher-z (alternative way of standardizing estimates).

* For each study, following dataset:

$$ \{(\hat{\theta}_k, \hat{\sigma}_k)\}_{k=1}^K $$
* Start with a dataset of *K* estimates across *N* studies, along with their standard errors.

* Each within-study estimate is standardized for comparability: divide coefficient by s.d. of outcome and multiply by s.d. of treatment.

* Here: Fisher-z (alternative way of standardizing estimates).

* For each study, following dataset:

$$ \{ (\hat{\theta}_k, \hat{\sigma}_k) \}_{k=1}^K $$

* Weight by precision of estimate.

$$ \bar{\theta} = \sum_{k=1}^{K} w_k \hat{\theta}_k, \quad w_k = \frac{1}{\hat{\sigma}_k^2} \frac{1}{\sum_{k=1}^{K} 1/\hat{\sigma}_k^2} $$
* Effect of democracy on growth:

* All estimates (188 studies, 2,047 estimates): 0.0440*** (0.0138).

* Main specifications (180 studies, 1,227 estimates): 0.0430*** (0.0141).

* Effect of HK on growth:

* All estimates (111 studies, 875 estimates): 0.1348*** (0.0156).

* Democracy-growth is 1/3 of human capital-growth.
* Does not depend on:

* Data structure: cross section, time series, panel.

* Estimation technique.

* Type of democracy index used.

* Country characteristics: human capital, investment, government size, corruption, ethnicity, religion, population.

* Depends on:

* Geography: stronger in Sub-Saharan Africa, weaker in high-income countries and South Asia.

* Time period: turmoil and decolonizations in the 1950s and 1960s.
Meta-Analytic Estimates: Heterogeneity

<table><caption>Democracy and growth, predicted values for selected regions and periods.</caption><thead><tr><th></th><th>All regions</th><th>Africa</th><th>East Asia</th><th>East Europe</th><th>Latin America</th><th>Middle East</th><th>South Asia</th><th>High-income</th></tr></thead><tbody><tr><td>All periods</td><td>0.1212*<br>(0.0558)</td><td>0.2039***<br>(0.0582)</td><td>-0.0007<br>(0.0719)</td><td>0.0430<br>(0.0626)</td><td>0.0162<br>(0.0607)</td><td>0.0592<br>(0.0606)</td><td>-0.0835<br>(0.0721)</td><td>0.0523<br>(0.0583)</td></tr><tr><td>1950-2009</td><td>0.0660<br>(0.0403)</td><td>0.1487***<br>(0.0440)</td><td>-0.0559<br>(0.0591)</td><td>-0.0122<br>(0.0492)</td><td>-0.0390<br>(0.0470)</td><td>0.0040<br>(0.0478)</td><td>-0.1387*<br>(0.0629)</td><td>-0.0029<br>(0.0439)</td></tr><tr><td>1960-2009</td><td>0.0710<br>(0.0367)</td><td>0.1537**<br>(0.0407)</td><td>-0.0509<br>(0.0577)</td><td>-0.0072<br>(0.0466)</td><td>-0.0340<br>(0.0440)</td><td>0.0090<br>(0.0449)</td><td>-0.1337*<br>(0.0599)</td><td>0.0021<br>(0.0409)</td></tr><tr><td>1970-2009</td><td>0.1315***<br>(0.0377)</td><td>0.2143**<br>(0.0415)</td><td>0.0096<br>(-0.0583)</td><td>0.0533<br>(0.0474)</td><td>0.0265<br>(0.0448)</td><td>0.0695<br>(0.0457)</td><td>-0.0732<br>(0.0605)</td><td>0.0626<br>(0.0417)</td></tr><tr><td>1980-2009</td><td>0.1739***<br>(0.0354)</td><td>0.2566***<br>(0.0394)</td><td>0.0520<br>(0.0567)</td><td>0.0957<br>(0.0454)</td><td>0.0689<br>(0.0428)</td><td>0.1119*<br>(0.0436)</td><td>-0.0308<br>(0.0590)</td><td>0.1050<br>(0.0395)</td></tr></tbody></table>
<figure><img src="image_224.png" /></figure>
* What is the impact of corruption on development?
* What is the impact of corruption on development?

* Vast literature, sometimes with contradictory findings.

* Generally negative effect of corruption on growth.

* But it varies by country (weaker effects when weak institutions).

* Also varies by type of corruption.

* Effect dependent upon how affect growth proximates (composition of investment, incentives to invest in physical and human capital...)
Corruption and Development

* What is the impact of corruption on development?

* Vast literature, sometimes with contradictory findings.

* Generally negative effect of corruption on growth.

* But it varies by country (weaker effects when weak institutions).

* Also varies by type of corruption.

* Effect dependent upon how affect growth proximates (composition of investment, incentives to invest in physical and human capital...)

⇒ Run a meta-analysis on this literature. Ugur (2014)
* Measuring corruption:

* Difficult to measure.

* Corruption practices might vary across country
* Measuring corruption:

* Difficult to measure.

* Corruption practices might vary across country

* Use a principal-agent definition of corruption:

* Agent: elected public official.

* Principal: other economic agents.

* Agent abuses authority by imposing surcharge on delivery of service to principal.

* Principal cannot hold agent accountable because of high monitoring cost.
* Measuring corruption:

* Difficult to measure.

* Corruption practices might vary across country

* Use a principal-agent definition of corruption:

* Agent: elected public official.

* Principal: other economic agents.

* Agent abuses authority by imposing surcharge on delivery of service to principal.

* Principal cannot hold agent accountable because of high monitoring cost.

* Captures bureaucratic and political corruption, bribes, nepotism, frauds, embezzlement.
* International Country Risk Guide (ICRG): index 0–6 capturing perceptions of corruption.

* Transparency International (TI): index 0–12 capturing information on corruption.

* World Governance Indicators (WGI): index -2.5–+2.5 capturing perceptions of corruption.
Qu et al. (2019)

* These scores are point estimates per country (averages across responses and data sources).

* They contain a standard error: magnitude of variation in underlying data.

* Large s.e. ⇒ large disagreement in underlying population.
Qu et al. (2019)

* These scores are point estimates per country (averages across responses and data sources).

* They contain a standard error: magnitude of variation in underlying data.

* Large s.e. ⇒ large disagreement in underlying population.

* WGI: 22 data sources. TI: 12 data sources.
Qu et al. (2019)

* These scores are point estimates per country (averages across responses and data sources).

* They contain a standard error: magnitude of variation in underlying data.

* Large s.e. ⇒ large disagreement in underlying population.

* WGI: 22 data sources. TI: 12 data sources.

* E.g. TI ranks Brunei #38 with point estimate of 60 and s.e. of 10.4.
  ⇒ 90% CI: Brunei as corrupt as Liberia (#83) or Canada (#9).
* Is this random noise or correlated with measurable factors?
* Is this random noise or correlated with measurable factors?

* Factors: different information across country, degree of corruption differs across country.
* Is this random noise or correlated with measurable factors?

* Factors: different information across country, degree of corruption differs across country.

* Important when aggregate indices, when assessing quality of measure.
$$SE = \frac{1}{\sqrt{N}} \left[ \frac{\sum_{n}^{N} (S - S_{n})^{2}}{N} \right]^{1/2}$$

* N: number of sources for the country.

* S_n: country score from source n.

* S: country mean score across N sources.
$$SE_i = \alpha + \beta * Z_i + \theta * \text{SOURCES}_i + \varepsilon_i$$

* Main indicator: WGI.

* Consider all sources in the WGI (5 sources for large coverage).

* 120 countries, separately for 2012, 2013, 2014.

* SOURCESᵢ: number of sources used to construct the WGI.
$$SE_i = \alpha + \beta * Z_i + \theta * \text{SOURCES}_i + \varepsilon_i$$

* Main indicator: WGI.

* Consider all sources in the WGI (5 sources for large coverage).

* 120 countries, separately for 2012, 2013, 2014.

* $\text{SOURCES}_i$: number of sources used to construct the WGI.

* $Z_i$:

* Anchoring: use past values (decrease variation).

* Economic variables (income, population, education). Halo effect: ambiguity in middle income, low education.

* Political variables: democracy, media freedom, fractionalization, inequality.
## Ratings Characteristics

<table><caption>Ratings characteristics.</caption><thead><tr><th></th><th>WGI-CC<br>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td><strong>Row 1</strong></td><td></td><td></td><td></td></tr><tr><td>Number of Data Sources</td><td>-0.0123***<br>(0.0023)</td><td>-0.0115***<br>(0.0021)</td><td>-0.0202***<br>(0.0037)</td></tr><tr><td># of Countries</td><td>195</td><td>195</td><td>195</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2123</td><td>0.2148</td><td>0.2835</td></tr><tr><td><strong>Row 2</strong></td><td></td><td></td><td></td></tr><tr><td>Initial Point Estimate</td><td>0.0785***<br>(0.0273)</td><td>0.0657***<br>(0.0227)</td><td>0.0863***<br>(0.0310)</td></tr><tr><td>Initial Point Estimate<br>Squared</td><td>-0.0177***<br>(0.0045)</td><td>-0.0161***<br>(0.0038)</td><td>-0.0188***<br>(0.0055)</td></tr><tr><td># of Countries</td><td>180</td><td>180</td><td>180</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2677</td><td>0.3161</td><td>0.2779</td></tr><tr><td><strong>Row 3</strong></td><td></td><td></td><td></td></tr><tr><td>Initial Point Estimate</td><td>0.0666**<br>(0.0315)</td><td>0.0569**<br>(0.0266)</td><td>0.0681*<br>(0.0360)</td></tr><tr><td>Initial Point Estimate<br>Squared</td><td>-0.0150**<br>(0.0058)</td><td>-0.0141***<br>(0.0052)</td><td>-0.0146**<br>(0.0070)</td></tr><tr><td># of Countries</td><td>171</td><td>171</td><td>171</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2351</td><td>0.2837</td><td>0.2616</td></tr><tr><td><strong>Row 4</strong></td><td></td><td></td><td></td></tr><tr><td>Number of Times<br>Covered</td><td>0.0052</td><td>0.0122***</td><td>0.0101*</td></tr><tr><td># of Countries</td><td>(0.0045)<br>195</td><td>(0.0031)<br>195</td><td>(0.0055)<br>195</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2107</td><td>0.2277</td><td>0.2857</td></tr></tbody></table>
## Demographic Characteristics

<table><caption>Demographic characteristics.</caption><thead><tr><th></th><th>WGI-CC<br>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>Row 1<br>Population</td><td>-0.0119<br>(0.0106)</td><td>-0.0083<br>(0.0098)</td><td>-0.0237**<br>(0.0109)</td></tr><tr><td># of Countries</td><td>193</td><td>193</td><td>193</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2182</td><td>0.2186</td><td>0.2956</td></tr><tr><td>Row 2<br>log GDP per capita (PPP)</td><td>0.3979**<br>(0.1689)</td><td>0.2043<br>(0.1453)</td><td>0.5599**<br>(0.2202)</td></tr><tr><td>log GDP per capita (PPP) squared</td><td>-0.0557**<br>(0.0223)</td><td>-0.0305<br>(0.0193)</td><td>-0.0785***<br>(0.0291)</td></tr><tr><td># of Countries</td><td>185</td><td>185</td><td>185</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2827</td><td>0.2730</td><td>0.3804</td></tr><tr><td>Row 3<br>GDP Growth</td><td>0.0047<br>(0.0032)</td><td>0.0034<br>(0.0029)</td><td>0.0038<br>(0.0026)</td></tr><tr><td>GDP Growth Squared</td><td>-0.0003<br>(0.0003)</td><td>-0.0001<br>(0.0002)</td><td>-0.0004<br>(0.0003)</td></tr><tr><td># of Countries</td><td>188</td><td>188</td><td>188</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2386</td><td>0.2464</td><td>0.2900</td></tr><tr><td>Row 4<br>Education (Bachelor's degree or<br>above)</td><td>-0.0036***<br>(0.0012)</td><td>-0.0022**<br>(0.0009)</td><td>-0.0026**<br>(0.0013)</td></tr><tr><td># of Countries</td><td>84</td><td>84</td><td>84</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2185</td><td>0.1039</td><td>0.2232</td></tr></tbody></table>
## Political Characteristics

Political characteristics.

<table><thead><tr><th></th><th>WGI, CC<br>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td><strong>Row 1</strong></td><td></td><td></td><td></td></tr><tr><td>Socialism</td><td>0.0067<br>(0.0097)</td><td>0.0009<br>(0.0091)</td><td>-0.0019<br>(0.0115)</td></tr><tr><td># of Countries</td><td>195</td><td>195</td><td>195</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2089</td><td>0.2107</td><td>0.2798</td></tr><tr><td><strong>Row 2</strong></td><td></td><td></td><td></td></tr><tr><td>Democracy</td><td>0.0382**<br>(0.0191)</td><td>0.0289<br>(0.0187)</td><td>0.0318<br>(0.0300)</td></tr><tr><td>Democracy squared</td><td>-0.0040**<br>(0.0017)</td><td>-0.0034**<br>(0.0017)</td><td>-0.0037<br>(0.0028)</td></tr><tr><td># of Countries</td><td>166</td><td>166</td><td>166</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.1866</td><td>0.2337</td><td>0.1457</td></tr><tr><td><strong>Row 3</strong></td><td></td><td></td><td></td></tr><tr><td>Media Freedom</td><td>-0.0613**<br>(0.0247)</td><td>-0.0613***<br>(0.0222)</td><td>-0.0376<br>(0.0242)</td></tr><tr><td># of Countries</td><td>171</td><td>171</td><td>171</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2386</td><td>0.2464</td><td>0.2900</td></tr></tbody></table>
## Heterogeneity Characteristics

<table><caption>Heterogeneity characteristics.</caption><thead><tr><th></th><th>WGI-CC<br>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>Row 1</td><td></td><td></td><td></td></tr><tr><td>Ethnic Fractionalization</td><td>0.0103<br>(0.0256)</td><td>0.0350<br>(0.0233)</td><td>-0.0261<br>(0.0312)</td></tr><tr><td># of Countries</td><td>182</td><td>182</td><td>182</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2325</td><td>0.2493</td><td>0.2999</td></tr><tr><td>Row 2</td><td></td><td></td><td></td></tr><tr><td>Religious Fractionalization</td><td>-0.0098<br>(0.0273)</td><td>-0.0256<br>(0.0246)</td><td>0.0044<br>(0.0288)</td></tr><tr><td># of Countries</td><td>188</td><td>188</td><td>188</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2351</td><td>0.2580</td><td>0.2985</td></tr><tr><td>Row 3</td><td></td><td></td><td></td></tr><tr><td>Linguistic Fractionalization</td><td>-0.0012<br>(0.0216)</td><td>0.0198<br>(0.0186)</td><td>0.0109<br>(0.0262)</td></tr><tr><td># of Countries</td><td>181</td><td>181</td><td>181</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2151</td><td>0.2426</td><td>0.2723</td></tr><tr><td>Row 4</td><td></td><td></td><td></td></tr><tr><td>Income Inequality</td><td>0.0012*<br>(0.0006)</td><td>0.0016**<br>(0.0006)</td><td>0.0019**<br>(0.0009)</td></tr><tr><td># of Countries</td><td>134</td><td>134</td><td>134</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.2433</td><td>0.2778</td><td>0.2320</td></tr></tbody></table>
* Search 20 databases for journal papers, working papers, reports, theses.

* Use 32 keywords for corruption, growth, developing countries.

* Use 43 low-income-country names.
* Search 20 databases for journal papers, working papers, reports, theses.

* Use 32 keywords for corruption, growth, developing countries.

* Use 43 low-income-country names.

* 1,042 hits: title/abstract screening ⇒ 338 studies.
* Search 20 databases for journal papers, working papers, reports, theses.

* Use 32 keywords for corruption, growth, developing countries.

* Use 43 low-income-country names.

* 1,042 hits: title/abstract screening ⇒ 338 studies.

* Critical evaluation: validity, reliability, applicability criteria.

* Include 40 empirical studies:

* 32 with estimates on per-capita GDP growth (29 with direct effects).

* 8 with estimates on per-capita GDP levels. Not used.

* For the 29 studies: extract all direct-effects estimates.
Partial correlations of corruption on growth

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

⇒ Meta-analytic estimate: -0.019* (0.010).
* What explains heterogeneity in estimates?

* Several potential factors:

* Method of study (OLS, IV) to solve endogeneity.

* Averaging period length: effect might take time to materialize.

* Level of development: less problematic at low levels?

* Source of data: ICGR directly asks business decision-makers (not anti-corruption groups).

* Publication type: published results might be biased toward finding strong effects.
<table><tr><td>Precision</td><td>0.017<br>(0.036)</td></tr><tr><td>Corruption data averaged<br>over more than 5 years</td><td>-0.060***<br>(0.015)</td></tr><tr><td>Low-income countries</td><td>-0.114***<br>(0.020)</td></tr><tr><td>ICGR corruption data</td><td>0.065***<br>(0.022)</td></tr><tr><td>Two-stage least-squares</td><td>0.023*<br>(0.014)</td></tr><tr><td>Journal paper</td><td>-0.107***<br>(0.017)</td></tr><tr><td>Constant</td><td>-1.063***<br>(0.218)</td></tr><tr><td>N</td><td>327</td></tr></table>
* Does corruption affect development and how can institutions solve this problem?

* Corrupted politicians and economic outcomes Prakash et al (2019)

* Electoral accountability and corruption Ferraz and Finan (2011)
Journal of Development Economics 141 (2019) 102370

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

ELSEVIER

Contents lists available at ScienceDirect

Journal of Development Economics

journal homepage: www.elsevier.com/locate/devec

<figure><img src="image_227.png" /></figure>

Do criminally accused politicians affect economic outcomes? Evidence from India*

Nishith Prakash <sup>a,*,</sup>, Marc Rockmore <sup>b</sup>, Yogesh Uppal <sup>c</sup>

<figure><img src="image_228.png" /></figure>
Corrupted Politicians and Economic Outcomes

* What is the cost of electing corrupted politicians?
# Corrupted Politicians and Economic Outcomes

* What is the cost of electing corrupted politicians?

* What is the aggregate economic cost of electing criminally accused politicians to State Legislative Assemblies in India in 2004–2008?

* Leverage Supreme Court of India order that all candidates disclose criminal background before 2004 elections.

* Measure constituency-level economic activity using satellite data on the intensity of night-time lights.

* Compare outcomes in constituencies that barely elected a criminally accused candidate vs not.

* Find that criminally accused politicians have large negative effects on economic activity within constituencies.
* Elected representatives in India:

* Each Indian State has a Legislative Assembly with single member constituencies (5 year terms).

* MLAs hold legislative, financial, executive powers, control the state bureaucracy, promotions, and job assignments.

* They play a key role in allocation of funds for development projects, distribution of licenses, and have access to discretionary funds.
# Background

* Elected representatives in India:

* Each Indian State has a Legislative Assembly with single member constituencies (5 year terms).

* MLAs hold legislative, financial, executive powers, control the state bureaucracy, promotions, and job assignments.

* They play a key role in allocation of funds for development projects, distribution of licenses, and have access to discretionary funds.

* Corruption and criminality in Indian politics:

* $100s of bn of corruption.

* To combat criminalization of Indian politics, all candidates required to issue criminal convictions and charges since 2003.

* In 2012, 30% of MLAs face criminal charges.
* Main challenge: victory of corrupt unlikely random, hence bias when comparing constituencies.

* Identification strategy:

* RD design based on victory margin (difference between accused winner and non-accused runner-up, and vice versa).

* Compare constituencies in which accused barely won vs those in which accused barely lost.

* Assumption: unobservables vary continuously across threshold.
* Specification:

$$GROWTH_{i,s,t+1} = \alpha + \gamma ACCUSED_{i,s,t} + f(MARGIN_{i,s,t}) + \mu_{i,s,t+1}$$

* $MARGIN_{i,s,t} \in (c-h, c+h)$ where $c=0$ and $h$ is bandwidth.

* $GROWTH_{i,s,t+1}$ is yearly growth rate in constituency $i$, state $s$, year $t+1$, defined as $\log(Y_{i,s,t+1}) - \log(Y_{i,s,t})$.

* $ACCUSED_{i,s,t}$ is the treatment.

* $f(\cdot)$ is a flexible polynomial in forcing variable.
* Need measurement at the state assembly constituency level.

* Proxy by night lights density:

* Shown to be a proxy for development in developing countries (correlation with income of 0.6).

* Available from NASA Meterological Satellites for each year since 1992.

* Image for 1km squares.

* Density: sum of light values from all pixels within constituency divided by area (aggregate).

* Also use length of roads built under a rural roads construction program as proxy for public goods provision at constituency level.
* Election Commission of India:

* Data on constituencies, candidates, turnout, results.

* Accusation information, along with number of criminal cases and charges.

* Focus on state elections between 2004 and 2008.

* Restrict to constituencies in which top-2 candidates are criminally accused and non-accused.

* Characteristics are balanced within a 5% margin.
<caption>Table 2</caption>

Balance test on pre-determined characteristics.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Top-2 Mixed Sample</th><th colspan="3">Top-2 Mixed Sample within 5% margin</th></tr><tr><th>Criminal</th><th>Non-Criminal</th><th>Diff.</th><th>Criminal</th><th>Non-Criminal</th><th>Diff.</th></tr></thead><tbody><tr><td>Growth of Light in (t-1)</td><td>21.988<br>(110.02)</td><td>33.736<br>(142.45)</td><td>-11.748**<br>(5.98)</td><td>26.037<br>(89.16)</td><td>24.503<br>(110.66)</td><td>1.534<br>(11.39)</td></tr><tr><td>Log Light in (t-1)</td><td>10.467<br>(2.33)</td><td>10.788<br>(1.65)</td><td>-0.321***<br>(0.12)</td><td>10.7<br>(1.40)</td><td>10.937<br>(1.41)</td><td>-0.237<br>(0.16)</td></tr><tr><td>Road Length (kms) in (t-1)</td><td>11.915<br>(25.06)</td><td>9.52<br>(10.54)</td><td>2.395<br>(1.74)</td><td>8.178<br>(6.52)</td><td>9.777<br>(9.94)</td><td>-1.599<br>(1.35)</td></tr><tr><td>Log Electorate Size in (t-1)</td><td>11.7<br>(0.85)</td><td>12.065<br>(0.41)</td><td>-0.364***<br>(0.04)</td><td>12.085<br>(0.44)</td><td>12.079<br>(0.39)</td><td>0.005<br>(0.05)</td></tr><tr><td>Log Number Voted in (t-1)</td><td>11.277<br>(0.78)</td><td>11.61<br>(0.37)</td><td>-0.332***<br>(0.04)</td><td>11.608<br>(0.37)</td><td>11.617<br>(0.36)</td><td>-0.009<br>(0.04)</td></tr><tr><td>Turnout in (t-1)</td><td>66.665<br>(12.11)</td><td>64.496<br>(11.13)</td><td>2.169***<br>(0.61)</td><td>63.042<br>(10.35)</td><td>64.043<br>(11.12)</td><td>-1.002<br>(1.22)</td></tr><tr><td>SC Reserved</td><td>0.142<br>(0.35)</td><td>0.126<br>(0.33)</td><td>0.016<br>(0.02)</td><td>0.12<br>(0.33)</td><td>0.072<br>(0.26)</td><td>0.048<br>(0.03)</td></tr><tr><td>ST Reserved</td><td>0.136<br>(0.34)</td><td>0.065<br>(0.22)</td><td>0.086***<br>(0.02)</td><td>0.032<br>(0.18)</td><td>0.036<br>(0.16)</td><td>0.005<br>(0.02)</td></tr><tr><td>Ruling Party in (t-1)</td><td>0.572<br>(0.50)</td><td>0.576<br>(0.50)</td><td>-0.004<br>(0.03)</td><td>0.475<br>(0.50)</td><td>0.526<br>(0.50)</td><td>-0.052<br>(0.06)</td></tr><tr><td>Incumbent in (t-1)</td><td>0.376<br>(0.48)</td><td>0.439<br>(0.50)</td><td>-0.063**<br>(0.03)</td><td>0.354<br>(0.48)</td><td>0.375<br>(0.49)</td><td>-0.021<br>(0.06)</td></tr><tr><td>Winner's Gender in (t-1)</td><td>0.065<br>(0.25)</td><td>0.057<br>(0.23)</td><td>0.008<br>(0.01)</td><td>0.07<br>(0.26)</td><td>0.072<br>(0.26)</td><td>-0.003<br>(0.03)</td></tr><tr><td>Runner-up's Gender in (t-1)</td><td>0.067<br>(0.25)</td><td>0.048<br>(0.21)</td><td>0.019<br>(0.01)</td><td>0.051<br>(0.22)</td><td>0.046<br>(0.21)</td><td>0.005<br>(0.02)</td></tr></tbody></table>

NOTES: Top-2 mixed sample includes candidates with 1 criminally accused against 1 non-accused in the top 2. Columns (3) and (6) have standard errors of the difference in the means of accused and non-accused MLAs in the parentheses. Asterisks denote significance levels (* = 0.10, ** = 0.05, *** = 0.01).
<figure><img src="image_229.png" /><figcaption>**Fig. 1.** Event Study: Effect of Electing Criminally Accused Politicians on Growth of Night Lights. Each line plot represents the raw averages of the growth of light density for that year in relation to t = 0, which is the year of the election.</figcaption></figure>
<figure><img src="image_230.png" /><figcaption>Fig. 2. Continuity of the Victory Margin between Criminally Accused and Non-Accused. The forcing variable is the margin of victory of a criminally-accused candidate. Negative values are the difference in the vote shares of a criminally-accused runners-up and a non-accused winner. Positive values are the differences in the vote shares of a criminally-accused winner and a non-accused runners-up. The estimated size of discontinuity in margin of victory (log difference in height) is -0.041 (se = 0.089).</figcaption></figure>
<figure><img src="image_231.png" /><figcaption>(a) Growth of Light in t-1</figcaption></figure>
<figure><img src="image_232.png" /><figcaption>(b) Growth of Light in t-2</figcaption></figure>
<figure><img src="image_233.png" /><figcaption>(c) Growth of Light in t-3</figcaption></figure>
<figure><img src="image_234.png" /><figcaption>(d) Electorate Size in t-1</figcaption></figure>
<figure><img src="image_235.png" /></figure>

(e) Number Voted in t-1
<figure><img src="image_236.png" /></figure>
<table><caption>**Table 3**
Effect of electing criminally accused politicians on growth of night lights.</caption><thead><tr><th rowspan="2">Dependent Variable</th><th colspan="4">Growth of Night Lights</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center; font-weight: bold;">Panel A</td></tr><tr><td>Criminally Accused</td><td>-24.33**<br>(10.23)</td><td>-21.56**<br>(8.83)</td><td>-28.36*<br>(15.52)</td><td>-14.75**<br>(6.30)</td></tr><tr><td>Bandwidth Size</td><td>6.16</td><td>7.79</td><td>3.08</td><td>12.32</td></tr><tr><td>No. of observations</td><td>1513</td><td>1742</td><td>744</td><td>2429</td></tr><tr><td>Bandwidth Type</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td></tr><tr><td>Polynomial order of control function</td><td>Local Linear</td><td></td><td></td><td></td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">Panel B</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">Estimated Percentage Change in GDP</td></tr><tr><td>Bickenbach et al. (2016) India-specific elasticity (0.10)</td><td>-2.4</td><td>-2.2</td><td>-2.8</td><td>-1.5</td></tr></tbody></table>
<table><caption>**Table 4**
Effect of electing criminally accused politicians by accusation type.</caption><thead><tr><th rowspan="2">Dependent Variable</th><th colspan="8">Growth of Night Lights</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><th colspan="9">PANEL A</th></tr><tr><th>Type of Accusation</th><th colspan="4">Financial Charge</th><th colspan="4">Non-Financial Charge</th></tr><tr><th>Local Linear</th><td>-43.84**<br>(18.77)</td><td>-34.73**<br>(15.87)</td><td>-52.01**<br>(26.29)</td><td>-26.29**<br>(12.20)</td><td>-12.19<br>(8.91)</td><td>-12.58<br>(9.37)</td><td>-18.25<br>(15.51)</td><td>-5.76<br>(5.27)</td></tr><tr><th>Bandwidth Size<br>No. of observations</th><td>8.04<br>519</td><td>10.27<br>632</td><td>4.02<br>278</td><td>16.08<br>867</td><td>8.59<br>1312</td><td>8.04<br>1272</td><td>4.30<br>736</td><td>17.19<br>2000</td></tr><tr><th colspan="9">PANEL B</th></tr><tr><th>Type of Accusation</th><th colspan="4">Serious Charge</th><th colspan="4">Non-Serious Charge</th></tr><tr><th>Local Linear</th><td>-25.98***<br>(9.82)</td><td>-24.80***<br>(8.97)</td><td>-20.31*<br>(11.65)</td><td>-15.09**<br>(6.83)</td><td>-15.28<br>(14.18)</td><td>-15.64<br>(14.81)</td><td>-22.59<br>(28.27)</td><td>-11.49<br>(8.43)</td></tr><tr><th>Bandwidth Size<br>No. of observations</th><td>5.94<br>1016</td><td>7.05<br>1125</td><td>2.97<br>511</td><td>11.89<br>1576</td><td>12.72<br>809</td><td>12.22<br>785</td><td>6.36<br>473</td><td>25.44<br>1051</td></tr><tr><th>Bandwidth Type</th><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td></tr><tr><th colspan="9">PANEL C</th></tr><tr><th colspan="9">Estimated Percentage Change in GDP</th></tr><tr><th></th><th colspan="4">Financial Charge</th><th colspan="4">Non-Financial Charge</th></tr><tr><th>Bickenbach et al. (2016)<br>Indian-specific elasticity (0.10)</th><td>-4.4<br>-3.5</td><td>-3.5<br>-5.2</td><td>-2.6</td><td>-1.2</td><td>-1.3</td><td>-1.8</td><td>-0.6</td><td>-0.6</td></tr><tr><th colspan="9">Serious Charge</th></tr><tr><th>Bickenbach et al. (2016)<br>India-specific elasticity (0.10)</th><td>-2.6<br>-2.5</td><td>-2.0<br>-2.0</td><td>-1.5</td><td>-1.5<br>-1.6</td><td>-1.6<br>-1.3</td><td>-2.3<br>-2.3</td><td>-1.1<br>-1.1</td><td>-1.1</td></tr></tbody></table>
<table><caption>Table 5
Effect of electing criminally accused politicians by multiple cases.</caption><thead><tr><th rowspan="3">Dependent Variable</th><th colspan="8">Growth of Night Lights</th></tr><tr><th colspan="4">Multiple Cases (≥ 2)</th><th colspan="4">Multiple Cases (≥ 5)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Local Linear</td><td>-32.10***<br>(12.30)</td><td>-27.71**<br>(11.25)</td><td>-32.84**<br>(15.86)</td><td>-19.67**<br>(8.36)</td><td>-66.47**<br>(27.09)</td><td>-51.81**<br>(22.99)</td><td>-90.15**<br>(36.57)</td><td>-43.39**<br>(18.88)</td></tr><tr><td>Bandwidth Size</td><td>7.05</td><td>8.47</td><td>3.52</td><td>14.09</td><td>7.29</td><td>10.50</td><td>3.65</td><td>14.58</td></tr><tr><td>No. of observations</td><td>770</td><td>870</td><td>393</td><td>1193</td><td>214</td><td>266</td><td>106</td><td>306</td></tr><tr><td>Bandwidth Type</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td></tr></tbody></table>
<table><caption>**Table 6**
Effect of electing criminally accused politicians by state characteristics.</caption><thead><tr><th rowspan="2">Dependent Variable</th><th colspan="4">Growth of Night Lights</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>PANEL A: BIMAROU States</strong></td></tr><tr><td>Criminally Accused</td><td>-50.68**<br>(22.49)</td><td>-44.01**<br>(19.80)</td><td>-47.52<br>(30.52)</td><td>-29.52**<br>(14.44)</td></tr><tr><td>Bandwidth Size</td><td>4.90</td><td>5.88</td><td>2.45</td><td>9.80</td></tr><tr><td>No. of observations</td><td>472</td><td>579</td><td>269</td><td>825</td></tr><tr><td colspan="5"><strong>PANEL B: Least Developed States</strong></td></tr><tr><td>Criminally Accused</td><td>-46.34**<br>(21.08)</td><td>-46.08**<br>(20.98)</td><td>-51.37*<br>(30.19)</td><td>-25.59*<br>(13.36)</td></tr><tr><td>Bandwidth Size</td><td>5.92</td><td>5.96</td><td>2.96</td><td>11.83</td></tr><tr><td>No. of observations</td><td>567</td><td>571</td><td>308</td><td>894</td></tr><tr><td colspan="5"><strong>PANEL C: High Corruption States</strong></td></tr><tr><td>Criminally Accused</td><td>-57.07**<br>(28.57)</td><td>-53.87**<br>(25.86)</td><td>-68.34<br>(44.57)</td><td>-36.78**<br>(18.08)</td></tr><tr><td>Bandwidth Size</td><td>6.09</td><td>7.37</td><td>3.05</td><td>12.19</td></tr><tr><td>No. of observations</td><td>432</td><td>478</td><td>210</td><td>691</td></tr><tr><td>Bandwidth Type</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td></tr><tr><td>Polynomial order of control function</td><td>Local Linear</td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 7**
Effect of electing criminally accused politicians by candidate characteristics.</caption><thead><tr><th rowspan="2">Dependent Variable</th><th colspan="8">Growth of Night Lights</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td colspan="9" style="text-align: center; font-weight: bold;">PANEL A</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Ruling Party</td></tr><tr><td>Criminally Accused</td><td>1.71<br>(3.86)</td><td>0.48<br>(4.11)</td><td>-1.40<br>(4.94)</td><td>3.05<br>(3.42)</td><td>-53.35**<br>(21.42)</td><td>-41.59***<br>(15.74)</td><td>-61.26*<br>(32.48)</td><td>-34.41***<br>(12.85)</td></tr><tr><td>Bandwidth Size</td><td>11.81</td><td>8.48</td><td>5.90</td><td>23.62</td><td>5.08</td><td>7.76</td><td>2.54</td><td>10.15</td></tr><tr><td>No. of observations</td><td>1150</td><td>932</td><td>683</td><td>1782</td><td>659</td><td>889</td><td>367</td><td>1063</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">PANEL B</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Above Median Wealth</td></tr><tr><td>Criminally Accused</td><td>-11.01<br>(9.12)</td><td>-10.48<br>(7.53)</td><td>-18.15<br>(15.83)</td><td>-7.89<br>(5.82)</td><td>-33.51**<br>(14.61)</td><td>-29.56**<br>(12.50)</td><td>-29.04<br>(17.96)</td><td>-19.92**<br>(9.58)</td></tr><tr><td>Bandwidth Size</td><td>11.21</td><td>14.5</td><td>5.6</td><td>22.42</td><td>5.93</td><td>7.75</td><td>2.97</td><td>11.87</td></tr><tr><td>No. of observations</td><td>1154</td><td>1314</td><td>698</td><td>1570</td><td>731</td><td>870</td><td>379</td><td>1151</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">PANEL C</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">College Educated</td></tr><tr><td>Criminally Accused</td><td>-20.86<br>(13.33)</td><td>-18.55<br>(11.42)</td><td>-26.65<br>(21.21)</td><td>-11.64<br>(8.21)</td><td>-19.39*<br>(10.36)</td><td>-22.04*<br>(11.93)</td><td>-32.23*<br>(16.93)</td><td>-12.64*<br>(7.14)</td></tr><tr><td>Bandwidth Size</td><td>6.61</td><td>8.54</td><td>3.31</td><td>13.22</td><td>10.30</td><td>8.61</td><td>5.15</td><td>20.59</td></tr><tr><td>No. of observations</td><td>887</td><td>1041</td><td>440</td><td>1384</td><td>961</td><td>821</td><td>554</td><td>1409</td></tr><tr><td>Bandwidth Type</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td><td>IK (h)</td><td>CCT</td><td>h/2</td><td>2h</td></tr></tbody></table>
<figure><img src="image_237.png" /></figure>
<figure><img src="image_238.png" /></figure>
American Economic Review 101 (June 2011): 1274–1311

http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.4.1274

Electoral Accountability and Corruption:
Evidence from the Audits of Local Governments

By CLAUDIO FERRAZ AND FREDERICO FINAN*
Ferraz and Finan (2011)

* Can political institutions mitigate corruption?
Ferraz and Finan (2011)

* Can political institutions mitigate corruption?

* Can electoral accountability mitigate corruption in local governments in Brazil in 2001–2004?
Ferraz and Finan (2011)

* Can political institutions mitigate corruption?

* Can electoral accountability mitigate corruption in local governments in Brazil in 2001–2004?

* Measure corruption through audits of municipalities for use of federal funds.

* Compare mayors serving in first vs second term (term limited).

* Find that reelection incentives reduce corruption.
* Political agency framework Besley (2006):

* Two types of political candidates: corrupt and non-corrupt.

* All provide public goods, but corrupt extract rents.

* Two periods, with voters infer actions of incumbent in period 1.

* Corrupt incumbents have incentives to reduce rent extraction to pass for an non-corrupt politician and be reelected.

⇒ Second term mayors should be more corrupt than first term mayors.
<figure><img src="image_239.png" /><figcaption>FIGURE 1. TIMELINE OF THE ELECTORAL CYCLES AND AUDIT TIMING</figcaption></figure>
* Brazilian mayors and their political horizons:

* Decentralization: $35 bn federal funds to local governments per year.

* Mayors decide how to spend these resources subject to some oversight.

* 73% run for reelection, but only 40% are reelected, so strong incentives to perform well in office.
* Brazilian mayors and their political horizons:

* Decentralization: $35 bn federal funds to local governments per year.

* Mayors decide how to spend these resources subject to some oversight.

* 73% run for reelection, but only 40% are reelected, so strong incentives to perform well in office.

* Corruption schemes in Brazil's municipalities:

* Fraud in procurement of goods and services, such as noncompetitive procurement process.

* Diversion of funds to purchase cars, apartments, pay friends' salaries.

* Overinvoicing.
* Brazil's anticorruption audit program:

* In 2003, anticorruption program: random auditing of municipal government expenditures.

* Total of 496 audited municipalities, randomly selected in each state (> 450m residents).

* Audits all federal funds transferred to the municipal government from 2001 onward.

* Report publicized and officials prosecuted.
* Define corruption as any irregularity in fraud, diversion, or overinvoicing per the reports.

* Measures of corruption:

* Amount of resources related to corrupt activities as a share of total amount of resources audited.

* Number of irregularities related to corruption.

* Share of service items associated with corruption.
<table><caption>TABLE I—SUMMARY STATISTICS OF CORRUPTION BY TYPE</caption><thead><tr><th rowspan="2"></th><th colspan="5">Type of irregularity</th></tr><tr><th>Diversion of funds</th><th>Illegal procurement</th><th>Overinvoicing</th><th>Corruption indicator</th><th>Mismanagement</th></tr></thead><tbody><tr><td>Proportion of municipalities with at least one irregularity</td><td>0.536<br>(0.499)</td><td>0.576<br>(0.495)</td><td>0.071<br>(0.258)</td><td>0.786<br>(0.411)</td><td>0.986<br>(0.116)</td></tr><tr><td>Conditional on at least one irregularity</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average number of irregularities</td><td>1.686<br>(1.006)</td><td>1.657<br>(0.945)</td><td>1.029<br>(0.171)</td><td>2.457<br>(1.554)</td><td>n/a</td></tr><tr><td>Average value of irregularity (R$)</td><td>159,205.20<br>(324,303.8)</td><td>291,431.50<br>(578,272.1)</td><td>60,670.14<br>(166,733.8)</td><td>327,573.10<br>(627,514.2)</td><td>n/a</td></tr><tr><td>Share of audited resources</td><td>0.041<br>(0.072)</td><td>0.070<br>(0.093)</td><td>0.015<br>(0.036)</td><td>0.080<br>(0.109)</td><td>n/a</td></tr><tr><td>Share of audited items</td><td>0.047<br>(0.036)</td><td>0.045<br>(0.028)</td><td>0.029<br>(0.012)</td><td>0.067<br>(0.050)</td><td>1.647<br>(1.154)</td></tr></tbody></table>

Notes: This table reports descriptive statistics on the various measures of corruption computed from the audit reports. These statistics presented in columns 1–4 were computed for our sample of 476 municipalities. In column 5, the statistics were computed for 366 municipalities. Standard deviations are reported in parentheses.
<table><caption>TABLE 2—SUMMARY STATISTICS OF CORRUPTION BY FIRST- AND SECOND-TERM MAYORS</caption><thead><tr><th rowspan="2"></th><th colspan="3">Share of audited resources</th><th colspan="3">Incidence of irregularities</th><th colspan="3">Share of audited items</th></tr><tr><th>First term (1)</th><th>Second term (2)</th><th>Difference (3)</th><th>First term (4)</th><th>Second term (5)</th><th>Difference (6)</th><th>First term (7)</th><th>Second term (8)</th><th>Difference (9)</th></tr></thead><tbody><tr><td>Diversion of funds</td><td>0.021</td><td>0.022</td><td>0.001<br>[0.005]</td><td>0.852</td><td>0.971</td><td>0.119<br>[0.103]</td><td>0.024</td><td>0.026</td><td>0.002<br>[0.003]</td></tr><tr><td>Illegal procurement</td><td>0.033</td><td>0.050</td><td>0.017<br>[0.007]</td><td>0.837</td><td>1.107</td><td>0.270<br>[0.100]</td><td>0.023</td><td>0.029</td><td>0.006<br>[0.003]</td></tr><tr><td>Overinvoicing</td><td>0.001</td><td>0.002</td><td>0.001<br>[0.001]</td><td>0.074</td><td>0.073</td><td>-0.001<br>[0.025]</td><td>0.002</td><td>0.002</td><td>0.000<br>[0.001]</td></tr><tr><td>Corruption</td><td>0.055</td><td>0.074</td><td>0.019<br>[0.009]</td><td>1.763</td><td>2.150</td><td>0.388<br>[0.157]</td><td>0.050</td><td>0.057</td><td>0.008<br>[0.005]</td></tr></tbody></table>

*Notes:* This table compares the various measures of corruption between first- and second-term mayors. These statistics were computed only for the 476 municipalities. Columns 1, 4, and 7 report the means for the 270 municipalities with a first-term mayor. Columns 2, 5, and 8 report the mean for the 206 municipalities with a second-term mayor. Columns 3, 6, and 9 report the difference in means, and robust standard errors of the difference are presented in brackets.
* Municipal characteristics: population, share urban, share with secondary schooling, GDP per capita, Gini, transfers.

* Mayor characteristics: gender, schooling, age.

* Political characteristics: legislators per voter, number of parties legislature, margin of victory in 2000, share legislators in mayor's party, number of audited items, total resources audited.

* Very few differences in characteristics between first and second-term mayors.
* Ideal experiment: randomly assign possibility of reelection across municipalities and measure difference in corruption levels across groups among first-term mayors.
* Ideal experiment: randomly assign possibility of reelection across municipalities and measure difference in corruption levels across groups among first-term mayors.

* Here: compare mayor in first term to mayors in second term:

$$r_i = \beta I_i + \mathbf{X}_i\phi + \mathbf{Z}_i\gamma + \varepsilon_i$$

* $r_i$: level of corruption in municipality i.

* $I_i$: indicator for first-term mayor.

* $\mathbf{X}_i$: municipal characteristics.

* $\mathbf{Z}_i$: mayor characteristics.
* Identification challenges:

* Without random assignment, unobserved characteristics of municipality and mayor may affect both reelection and corruption (political ability or campaigning effort).

* Effect of experience of second term on corruption.
* Strategy: electoral RDD.

* Compare municipalities where incumbent mayors barely won reelection in 2000 (term 2 in 2001–2004) to municipalities where incumbent barely lost (term 1 in 2001–2004).

* Creates quasi-random assignment of municipalities with first vs second term mayors.

* Control for unobservable characteristics of municipality that determine both reelection and corruption level (ideology of voters).
* Strategy: electoral RDD.

* Compare municipalities where incumbent mayors barely won reelection in 2000 (term 2 in 2001–2004) to municipalities where incumbent barely lost (term 1 in 2001–2004).

* Creates quasi-random assignment of municipalities with first vs second term mayors.

* Control for unobservable characteristics of municipality that determine both reelection and corruption level (ideology of voters).

* Control for political ability: compare second-term mayors to first-term mayors who were reelected in 2004.
* Strategy: electoral RDD.

* Compare municipalities where incumbent mayors barely won reelection in 2000 (term 2 in 2001–2004) to municipalities where incumbent barely lost (term 1 in 2001–2004).

* Creates quasi-random assignment of municipalities with first vs second term mayors.

* Control for unobservable characteristics of municipality that determine both reelection and corruption level (ideology of voters).

* Control for political ability: compare second-term mayors to first-term mayors who were reelected in 2004.

* Control for experience: gather data on political position held since 1989, and compare second-term mayors to first-term mayors who had previous political experience.
<table><caption>TABLE 4—THE EFFECTS OF REELECTION INCENTIVES ON CORRUPTION</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="8">Share of audited resources involving corruption</th></tr><tr><th>OLS (1)</th><th>OLS (2)</th><th>OLS (3)</th><th>OLS (4)</th><th>OLS (5)</th><th>OLS (6)</th><th>Matching (7)</th><th>Tobit (8)</th></tr></thead><tbody><tr><td>Mayor in first term</td><td>-0.019<br/>[0.009]<sup>**</sup></td><td>-0.020<br/>[0.010]<sup>**</sup></td><td>-0.020<br/>[0.010]<sup>**</sup></td><td>-0.024<br/>[0.011]<sup>**</sup></td><td>-0.026<br/>[0.011]<sup>**</sup></td><td>-0.027<br/>[0.011]<sup>**</sup></td><td>-0.028<br/>[0.010]<sup>**</sup></td><td>-0.042<br/>[0.012]<sup>**</sup></td></tr><tr><td>R<sup>2</sup></td><td>0.01</td><td>0.08</td><td>0.10</td><td>0.12</td><td>0.14</td><td>0.20</td><td>n/a</td><td>n/a</td></tr><tr><td>Observations</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td></tr><tr><td>Mayor characteristics</td><td>No</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>Municipal characteristics</td><td>No</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>Political and judicial institutions</td><td>No</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>Lottery intercepts</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>State intercepts</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>
<table><caption>TABLE 5—THE EFFECTS OF REELECTION INCENTIVES ON ALTERNATIVE MEASURES OF CORRUPTION</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="4">Numbers of irregularities involving corruption</th><th colspan="4">Share of audited items involving corruption</th></tr><tr><th>OLS (1)</th><th>OLS (2)</th><th>Matching (3)</th><th>Negative bionomial (4)</th><th>OLS (5)</th><th>OLS (6)</th><th>Matching (7)</th><th>Tobit (8)</th></tr></thead><tbody><tr><td>Mayor in first term</td><td>-0.388<br>[0.158]<sup>**</sup></td><td>-0.471<br>[0.148]<sup>***</sup></td><td>-0.339<br>[0.146]<sup>**</sup></td><td>-0.456<br>[0.127]<sup>**</sup></td><td>-0.008<br>[0.005]</td><td>-0.011<br>[0.004]<sup>**</sup></td><td>-0.010<br>[0.005]<sup>a</sup></td><td>-0.009<br>[0.005]<sup>a</sup></td></tr><tr><td>R<sup>2</sup></td><td>0.01</td><td>0.43</td><td>n/a</td><td>n/a</td><td>0.01</td><td>0.45</td><td>n/a</td><td>n/a</td></tr><tr><td>Observations</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td><td>476</td></tr><tr><td>Mayor characteristics</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Municipal characteristics</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Political and judicial institutions</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Lottery intercepts</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>State intercepts</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>
<table><caption>TABLE 6—THE EFFECT OF REELECTION INCENTIVES ON CORRUPTION, CONTROLLING FOR UNOBSERVED MUNICIPAL CHARACTERISTICS</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="7">Share of audited resources involving corruption</th></tr><tr><th>Incumbents who run for reelection in 2000 (1)</th><th>Linear (2)</th><th>Quadratic (3)</th><th>Cubic (4)</th><th>Linear spline (5)</th><th>Quadratic spline (6)</th><th>Cubic spline (7)</th></tr></thead><tbody><tr><td>Mayor in first term</td><td>-0.031<br/>[0.014]**</td><td>-0.039<br/>[0.019]**</td><td>-0.040<br/>[0.019]**</td><td>-0.038<br/>[0.022]*</td><td>-0.043<br/>[0.019]**</td><td>-0.047<br/>[0.024]*</td><td>-0.028<br/>[0.029]</td></tr><tr><td>Observations</td><td>328</td><td>328</td><td>328</td><td>328</td><td>328</td><td>328</td><td>328</td></tr><tr><td>R<sup>2</sup></td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.28</td><td>0.28</td></tr><tr><td>Mayor characteristics</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>Municipal characteristics</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>Political and judicial institutions</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>Lottery intercepts</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>State intercepts</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>
<figure><img src="image_240.png" /><figcaption>FIGURE 2. THE EFFECTS OF REELECTION INCENTIVES ON CORRUPTION</figcaption></figure>