# 1. Data Quality vs Data Coverage

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.48***<br>[0.05]</td><td>0.29***<br>[0.07]</td><td>0.37***<br>[0.06]</td><td>0.27***<br>[0.08]</td><td>0.41***<br>[0.06]</td></tr><tr><td>ILO variable</td><td>EST</td><td>YI</td><td>YI</td><td>YII</td><td>YII</td></tr><tr><td>Time coverage</td><td>1990-2017</td><td>1956-2017</td><td>1990-2017</td><td>1956-2017</td><td>1990-2017</td></tr><tr><td>COB coverage</td><td>135</td><td>132</td><td>132</td><td>133</td><td>133</td></tr><tr><td>Observations</td><td>280,003</td><td>240,333</td><td>222,292</td><td>320,044</td><td>272,104</td></tr></tbody></table>

• Different sample:

• (2) → (3) = +0.08 (+28%)

• (4) → (5) = +0.14 (+52%)
# 1. Data Quality vs Data Coverage

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.48***<br>[0.05]</td><td>0.29***<br>[0.07]</td><td>0.37***<br>[0.06]</td><td>0.27***<br>[0.08]</td><td>0.41***<br>[0.06]</td></tr><tr><td>ILO variable</td><td>EST</td><td>YI</td><td>YI</td><td>YII</td><td>YII</td></tr><tr><td>Time coverage</td><td>1990-2017</td><td>1956-2017</td><td>1990-2017</td><td>1956-2017</td><td>1990-2017</td></tr><tr><td>COB coverage</td><td>135</td><td>132</td><td>132</td><td>133</td><td>133</td></tr><tr><td>Observations</td><td>280,003</td><td>240,333</td><td>222,292</td><td>320,044</td><td>272,104</td></tr></tbody></table>

* Attenuation bias:

* (1) → (3) = -0.11 (-23%)

* (1) → (5) = -0.07 (-15%)

* But regression samples still not comparable.
# 1. Data Quality vs Data Coverage

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.48***<br>[0.05]</td><td>0.45***<br>[0.07]</td><td>0.37***<br>[0.06]</td></tr><tr><td>ILO variable</td><td>EST</td><td>EST</td><td>YI/YII</td></tr><tr><td>Time coverage</td><td>1990-2017</td><td>1990-2017</td><td>1990-2017</td></tr><tr><td>COB coverage</td><td>135</td><td>132</td><td>132</td></tr><tr><td>Observations</td><td>280,003</td><td>222,292</td><td>222,292</td></tr></tbody></table>

* Different sample: (1) → (2) = -0.03 (-6%)

* Attenuation bias: (2) → (3) = -0.08 (-18%)
# 1. Data Quality vs Data Coverage

<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 FLFP 15+</td><td>0.45***<br>[0.07]</td><td>0.38***<br>[0.07]</td><td>0.38***<br>[0.07]</td><td>0.26***<br>[0.06]</td></tr><tr><td>ILO variable</td><td>EST</td><td>YI/YII</td><td>EST</td><td>YI/YII</td></tr><tr><td>Time coverage</td><td>1990-2017</td><td>1990-2017</td><td>1990-2017</td><td>1990-2017</td></tr><tr><td>COB coverage</td><td>109</td><td>109</td><td>109</td><td>109</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>218,832</td><td>218,832</td><td>218,832</td><td>218,832</td></tr></tbody></table>

## Attenuation bias:

* No controls: (1) → (2) = -0.07 (-16%)

* Controls: (3) → (4) = -0.12 (-32%)
# 1. Data Quality vs Data Coverage

How different are these estimates?

* Ideally, estimate covariance matrix via seemingly unrelated regression.

* Shortcut:

* Take the estimates difference.

* Compute s.e. around this difference: $\sqrt{se_1^2 + se_2^2}$.

* Assumes zero covariance for *parameter* estimates.

* Good approximation.
# 1. Data Quality vs Data Coverage

<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 FLFP 15+</td><td>0.45***<br>[0.07]</td><td>0.38***<br>[0.07]</td><td>0.38***<br>[0.07]</td><td>0.26***<br>[0.06]</td></tr><tr><td>ILO variable</td><td>EST</td><td>YI/YII</td><td>EST</td><td>YI/YII</td></tr><tr><td>Time coverage</td><td>1990-2017</td><td>1990-2017</td><td>1990-2017</td><td>1990-2017</td></tr><tr><td>COB coverage</td><td>109</td><td>109</td><td>109</td><td>109</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>218,832</td><td>218,832</td><td>218,832</td><td>218,832</td></tr></tbody></table>

## Attenuation bias:

* No controls: (1) → (2) = -0.07, s.e. ≈ 0.96.

* Controls: (3) → (4) = -0.12, s.e. ≈ 0.95.
## 2. Controls and Comparison Groups

* Controls build implicit comparison groups.

* If correlated with X and Y, capture mediating channels.

* Comparison groups:

* Individual and spouse characteristics.

* COB characteristics.

* Residence location.

* Cultural Proxy: FLFP 15+, ILO Estimates (EST), 1990–2017.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.
## 2. Controls and 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: 62 categories (1956–2017).

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

* Spouse characteristics:

* Same as above.

* Employment status (indicator).

* Citizenship status (indicator).

* Total income in 1999 US$.
## 2. Controls and Comparison Groups

* 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
## 2. Controls and Comparison Groups

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.48***<br>[0.05]</td><td>0.50***<br>[0.06]</td><td>0.40***<br>[0.07]</td><td>0.43***<br>[0.11]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Spouse</td><td>No</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>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Residence level</td><td>State</td><td>State</td><td>State</td><td>State</td><td>State</td></tr><tr><td>Residence FE</td><td>51</td><td>51</td><td>51</td><td>51</td><td>51</td></tr><tr><td>Observations</td><td>280,003</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Different source of variation ($\approx 12k$): (1) $\rightarrow$ (2) = +0.02 (+4%).

* Mediating factors: (2) $\rightarrow$ (5) = -0.08 (-16%). s.e. $\approx 0.09$.
3. Interpretation: Magnitude

* How to gauge effect size in a given study? Depends on context.

* Usually most useful: share of variation explained.

* ↑ 1 s.d. treatment is associated with Δ x s.d. of outcome.

* s.d. relative to which sample? Depends on context.

* Relative to regression sample? Here, individuals.

* Relative to sample of interest? Here, COB.

* Tied to effective regression sample? Here, residualized s.d.
Mummolo and Peterson (2018)

* Still, give actual estimates: meaning depends on context.
3. Interpretation: Magnitude

"Cohen's d" (arbitrary rule of thumb):

* $x \le 0.2$: small effect.

* $0.2 \le x \le 0.8$: medium effect.

* $x > 0.8$: large effect.
## 3. Interpretation: Magnitude

<table><thead><tr><th rowspan="2">Sample:</th><th colspan="4">Individuals</th><th colspan="3">COB</th></tr><tr><th>Obs.</th><th>Mean</th><th>S.d.</th><th>S.d.</th><th>Obs.</th><th>Mean</th><th>S.d.</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>268,452</td><td>0.45</td><td>0.12</td><td>0.05</td><td>109</td><td>0.49</td><td>0.14</td></tr><tr><td>Active</td><td>268,452</td><td>0.52</td><td>0.50</td><td>0.47</td><td>109</td><td>0.57</td><td>0.13</td></tr></tbody></table>

* Initial question: explain $\Delta$ in economic behavior across cultures.

* Cultures are captured here by COB.

* Relevant statistics are at the COB level.

* Mechanically larger magnitude ($\times 4$).
3. Interpretation: Magnitude

* Cultural Proxy: FLFP 15+, ILO Estimates (EST), 1990–2017.

• Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
3. Interpretation: Magnitude

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.50***<br>[0.06]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Outcome mean</td><td>0.57</td><td>0.57</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>0.13</td></tr><tr><td>COB FLFP 15+ mean</td><td>0.49</td><td>0.49</td></tr><tr><td>COB FLFP 15+ s.d.</td><td>0.14</td><td>0.14</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td></tr></tbody></table>

• Estimate

• (1): ↑ 1pp COB FLFP associated with ↑ 0.5pp Active.

• (2): ↑ 1pp COB FLFP associated with ↑ 0.4pp Active.
3. Interpretation: Magnitude

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.50***<br>[0.06]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Outcome mean</td><td>0.57</td><td>0.57</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>0.13</td></tr><tr><td>COB FLFP 15+ mean</td><td>0.49</td><td>0.49</td></tr><tr><td>COB FLFP 15+ s.d.</td><td>0.14</td><td>0.14</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Magnitude (mean)

• (1): ↑ 10pp COB FLFP associated with ↑ 9% of mean in Active.

• (2): ↑ 10pp COB FLFP associated with ↑ 7% of mean in Active.
3. Interpretation: Magnitude

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.50***<br>[0.06]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Outcome mean</td><td>0.57</td><td>0.57</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>0.13</td></tr><tr><td>COB FLFP 15+ mean</td><td>0.49</td><td>0.49</td></tr><tr><td>COB FLFP 15+ s.d.</td><td>0.14</td><td>0.14</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Magnitude (s.d.)

• (1): ↑ 1 s.d. COB FLFP associated with ↑ 0.54 s.d. in Active.

• (2): ↑ 1 s.d. COB FLFP associated with ↑ 0.45 s.d. in Active.

* Cohen's *d*: effect size is 0.5σ (medium).
3. Interpretation: Magnitude

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.50***<br>[0.06]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Outcome mean</td><td>0.57</td><td>0.57</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>0.13</td></tr><tr><td>COB FLFP 15+ mean</td><td>0.49</td><td>0.49</td></tr><tr><td>COB FLFP 15+ s.d.</td><td>0.14</td><td>0.14</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Relative magnitudes

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

* ↓ 1 s.d. children (0.44) associated with ↑ 0.18 s.d. in Active.
3. Interpretation: Precision

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.50 ± 0.11<br>[0.06]</td><td>0.42 ± 0.15<br>[0.07]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* $x \pm y/2$, where y is width of 95% confidence interval.

* Abadie (2019) Statistical Non-Significance
4. Clustering

* Cultural Proxy: FLFP 15+, ILO Estimates (EST), 1990–2017.

• Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Residence location FE: State.

* Clustering: country, State, COB, both?

* Cameron and Miller (2015) Abadie et al. (2017)
# 4. Clustering

* Regressor correlated across groups ⇌ cluster on these groups.

* One-way clustering: inflation factor of standard errors increases with

* Cluster size.

* Within-cluster correlation of regressor.

* Within-cluster correlation of error.

* Two-way clustering (for one coefficient):

* Estimate s.e. for both one-way clustering separately.

* Estimate s.e. for one-way clustering on intersection.

* Compute $\sqrt{se_1^2 + se_2^2 - se_{1\cap2}^2}$.
# 4. Clustering

<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 FLFP 15+</td><td>0.42***<br>[0.02]</td><td>0.42***<br>[0.04]</td><td>0.42***<br>[0.07]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Cluster level</td><td>None</td><td>State</td><td>COB</td><td>S+COB</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>

51 States of residence, 109 countries of birth.
# 5. Source of Identification

* Cultural Proxy: FLFP 15+, ILO Estimates (EST), 1990–2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
# 5. Source of Identification

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.50***<br>[0.06]</td><td>0.42***<br>[0.07]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td></tr><tr><td>State FE</td><td>Yes</td><td>Yes</td></tr><tr><td>COB FLFP 15+ variance</td><td>0.013</td><td>0.013</td></tr><tr><td>COB FLFP 15+ residual variance</td><td>0.013</td><td>0.002</td></tr><tr><td>Adj. R2</td><td>0.026</td><td>0.106</td></tr><tr><td>Adj. within R2</td><td>0.013</td><td>0.036</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Residual variance: variance conditional on controls.

* (2): residual variance = 18% of variance.
  ⇒ 18% of original variation left for identification.
5. Source of Identification

* Implicit regression weight: share of total residual variance.

* Aronow and Samii (2016) How representative are OLS estimates?

* Compute for each cultural group its contribution.

* Share of residual variance of each COB.
# 5. Source of Identification: Effective Sample

<table><thead><tr><th rowspan="2">Sample (%)</th><th colspan="3">Regression weight (%)</th></tr><tr><th>No controls</th><th>Controls</th><th></th></tr></thead><tbody><tr><td>MEX</td><td>38.64</td><td>23.24</td><td>TUR 6.39</td></tr><tr><td>IND</td><td>8.23</td><td>10.78</td><td>CHL 5.49</td></tr><tr><td>CHN</td><td>5.11</td><td>9.73</td><td>ISR 5.41</td></tr><tr><td>PHL</td><td>3.59</td><td>9.56</td><td>NPL 4.41</td></tr><tr><td>SLV</td><td>3.05</td><td>4.29</td><td>HUN 4.06</td></tr><tr><td>KOR</td><td>3.04</td><td>3.75</td><td>GHA 3.61</td></tr><tr><td>JPN</td><td>2.21</td><td>3.02</td><td>PER 3.36</td></tr><tr><td>GTM</td><td>1.95</td><td>2.86</td><td>COL 3.07</td></tr><tr><td>CAN</td><td>1.92</td><td>2.25</td><td>IND 3.05</td></tr><tr><td>VNM</td><td>1.77</td><td>1.96</td><td>JPN 2.73</td></tr><tr><td>TUR</td><td>0.32</td><td>0.92</td><td>MEX 1.86</td></tr><tr><td>CHL</td><td>0.24</td><td>0.16</td><td>IND 3.05</td></tr><tr><td>ISR</td><td>0.34</td><td>0.24</td><td>CHN 1.87</td></tr></tbody></table>

* Effective sample very different from nominal sample.

* Good news: contribution of each COB effectively balanced.
# 5. Source of Identification: Nominal Sample

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

* Nominal weight (%) across 109 COB.
# 5. Source of Identification: Effective Sample

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

* Regression weight (%) across 109 COB.
# 6. Outliers

* Cultural Proxy: FLFP 15+, ILO Estimates (EST), 1990–2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.

* Outliers: sample size, residual variance.
# 6. Outliers: Nominal Sample

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.42***<br>[0.07]</td><td>0.41***<br>[0.07]</td><td>0.40***<br>[0.08]</td><td>0.37***<br>[0.08]</td><td>0.31***<br>[0.07]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>COB dropped level</td><td>None</td><td>MEX</td><td>IND</td><td>CHN</td><td>All</td></tr><tr><td>Observations</td><td>268,452</td><td>171,098</td><td>245,988</td><td>253,365</td><td>133,547</td></tr></tbody></table>

* Different source of variation (50% of sample).

* $(1) \rightarrow (5) = -0.11 (-26\%).$ s.e. $\approx 0.10$.

* China outlier: very high FLFP at home (communism).
# 6. Outliers: Effective Sample

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.42***<br>[0.07]</td><td>0.40***<br>[0.08]</td><td>0.41***<br>[0.08]</td><td>0.38***<br>[0.08]</td><td>0.33***<br>[0.09]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>COB dropped level</td><td>None</td><td>TUR</td><td>CHL</td><td>ISR</td><td>All</td></tr><tr><td>Observations</td><td>268,452</td><td>267,507</td><td>267,776</td><td>267,530</td><td>265,909</td></tr></tbody></table>

* Different source of variation (99% of sample).

* (1) → (5) = -0.09 (-21%). s.e. ≈ 0.12.
# 7. Choice of Cultural Proxy

* Cultural Proxy: FLFP 15+ vs 15-65, FLFP vs ratio male to female.

* Data source: ILO Estimates (EST), 1990-2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
# 7. Choice of Cultural Proxy

<table><thead><tr><th rowspan="3">Dependent variable:<br>COB measure:</th><th colspan="4">Active</th></tr><tr><th colspan="2">FLFP</th><th colspan="2">M to F LFP</th></tr><tr><th>15+<br>(1)</th><th>15-65<br>(2)</th><th>15+<br>(3)</th><th>15-65<br>(4)</th></tr></thead><tbody><tr><td>COB measure</td><td>0.42***<br>[0.07]</td><td>0.34***<br>[0.07]</td><td>0.37***<br>[0.06]</td><td>0.33***<br>[0.06]</td></tr><tr><td>Outcome mean</td><td>0.57</td><td>0.57</td><td>0.57</td><td>0.57</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>0.13</td><td>0.13</td><td>0.13</td></tr><tr><td>COB measure mean</td><td>0.49</td><td>0.55</td><td>0.67</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.14</td><td>0.16</td><td>0.19</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>
# 7. Choice of Cultural Proxy

## Measurement:

* 15–65 vs 15+: less bias from mortality.

* Ratio male to female LFP:

* Comparable measurement across countries.

* Captures COB gender roles more directly.

## Results

<table><thead><tr><th rowspan="2"></th><th colspan="2">FLFP</th><th colspan="2">M to F LFP</th></tr><tr><th>15+</th><th>15-65</th><th>15+</th><th>15-65</th></tr></thead><tbody><tr><th>Δ pp</th><td>4.2</td><td>3.4</td><td>3.7</td><td>3.3</td></tr><tr><th>Δ sd</th><td>0.45</td><td>0.42</td><td>0.54</td><td>0.53</td></tr></tbody></table>
# 8. Choice of Outcome

* Cultural Proxy: male to female LFP 15–65, ILO EST, 1990–2017.

* Controls: individual, spouse, COB.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
# 8. Choice of Outcome

* Extensive margin:

* Indicator for *In the labor force*. LABFORCE

* Indicator for *Employed*. EMPSTAT

* Indicator for *Worked for pay last week*. WRKLSTWK

* Indicator for *Worked for pay last year*. WORKEDYR

* Intensive margin:

* Number of *Weeks worked last year*. WKSWORK2

* Number of *Usual hours worked per week*. UHRSWORK

* Within-household division of labor.
# 8. Choice of Outcome: Extensive Margin

<table><thead><tr><th rowspan="2">Dependent variable:</th><th>Active</th><th>Employed</th><th>Work LW</th><th>Work LY</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>0.33***<br>[0.06]</td><td>0.35***<br>[0.06]</td><td>0.36***<br>[0.06]</td><td>0.38***<br>[0.05]</td></tr><tr><td>Outcome mean</td><td>0.57</td><td>0.52</td><td>0.50</td><td>0.59</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>0.13</td><td>0.13</td><td>0.14</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>257,217</td><td>268,452</td></tr></tbody></table>

* ↑ 1 s.d. COB measure associated with ↑ 0.53–0.58 s.d. in outcome.
## 8. Choice of Outcome: Intensive Margin

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Weeks</th><th colspan="2">Hours</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>18.55***<br>[2.66]</td><td>4.32***<br>[1.11]</td><td>16.29***<br>[2.27]</td><td>4.80***<br>[0.79]</td></tr><tr><td>Sample</td><td>All</td><td>Working</td><td>All</td><td>Working</td></tr><tr><td>Outcome mean</td><td>25.25</td><td>42.74</td><td>21.17</td><td>35.77</td></tr><tr><td>Outcome s.d.</td><td>6.18</td><td>2.32</td><td>5.36</td><td>1.86</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>145,964</td><td>268,452</td><td>145,964</td></tr></tbody></table>

* Include zeros:

* ↑ 1 s.d. COB measure associated with ↑ 3.9 weeks per year.

* ↑ 1 s.d. COB measure associated with ↑ 0.63 s.d. weeks per year.
# 8. Choice of Outcome: Intensive Margin

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Weeks</th><th colspan="2">Hours</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>18.55***<br>[2.66]</td><td>4.32***<br>[1.11]</td><td>16.29***<br>[2.27]</td><td>4.80***<br>[0.79]</td></tr><tr><td>Sample</td><td>All</td><td>Working</td><td>All</td><td>Working</td></tr><tr><td>Outcome mean</td><td>25.25</td><td>42.74</td><td>21.17</td><td>35.77</td></tr><tr><td>Outcome s.d.</td><td>6.18</td><td>2.32</td><td>5.36</td><td>1.86</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>145,964</td><td>268,452</td><td>145,964</td></tr></tbody></table>

## • Include zeros:

* ↑ 1 s.d. COB measure associated with ↑ 3.4 hours per week.

* ↑ 1 s.d. COB measure associated with ↑ 0.64 s.d. hours per week.
# 8. Choice of Outcome: Intensive Margin

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Weeks</th><th colspan="2">Hours</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>18.55***<br>[2.66]</td><td>4.32***<br>[1.11]</td><td>16.29***<br>[2.27]</td><td>4.80***<br>[0.79]</td></tr><tr><td>Sample</td><td>All</td><td>Working</td><td>All</td><td>Working</td></tr><tr><td>Outcome mean</td><td>25.25</td><td>42.74</td><td>21.17</td><td>35.77</td></tr><tr><td>Outcome s.d.</td><td>6.18</td><td>2.32</td><td>5.36</td><td>1.86</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>145,964</td><td>268,452</td><td>145,964</td></tr></tbody></table>

* Excluding zeros:

* ↑ 1 s.d. COB measure associated with ↑ 0.9 week per year.

* ↑ 1 s.d. COB measure associated with ↑ 0.39 s.d. week per year.
# 8. Choice of Outcome: Intensive Margin

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Weeks</th><th colspan="2">Hours</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>18.55***<br>[2.66]</td><td>4.32***<br>[1.11]</td><td>16.29***<br>[2.27]</td><td>4.80***<br>[0.79]</td></tr><tr><td>Sample</td><td>All</td><td>Working</td><td>All</td><td>Working</td></tr><tr><td>Outcome mean</td><td>25.25</td><td>42.74</td><td>21.17</td><td>35.77</td></tr><tr><td>Outcome s.d.</td><td>6.18</td><td>2.32</td><td>5.36</td><td>1.86</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>145,964</td><td>268,452</td><td>145,964</td></tr></tbody></table>

* Excluding zeros:

* ↑ 1 s.d. COB measure associated with ↑ 1.0 hour per week.

* ↑ 1 s.d. COB measure associated with ↑ 0.54 s.d. hour per week.
8. Choice of Outcome: Household Division of labor

<table><thead><tr><th rowspan="3">Dependent variable:</th><th colspan="5">Wife - Husband</th></tr><tr><th>Employed</th><th colspan="2">Weeks</th><th colspan="2">Hours</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB M/F LFP 15-65</td><td>0.35***<br>[0.06]</td><td>18.11***<br>[2.59]</td><td>3.17***<br>[0.99]</td><td>15.53***<br>[2.40]</td><td>3.32***<br>[1.13]</td></tr><tr><td>Sample</td><td>All</td><td>All</td><td>Working</td><td>All</td><td>Working</td></tr><tr><td>Outcome mean</td><td>-0.36</td><td>-18.96</td><td>-4.60</td><td>-19.31</td><td>-7.73</td></tr><tr><td>Outcome s.d.</td><td>0.13</td><td>5.70</td><td>2.33</td><td>5.56</td><td>2.90</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td><td>0.69</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>137,039</td><td>268,452</td><td>137,039</td></tr></tbody></table>

* Same magnitudes for within household division of labor.
# 9. Regression Model

* Cultural Proxy: male to female LFP 15-65, ILO EST, 1990-2017.

* No controls.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
# 9. Regression Model

* Linear probability model:

* OLS.

* reghdfe High-dimensional FE, multi-way clustering. Correia (2017)

* Good approximation for small changes.

* Nicer interpretation.

* Non-linear probability model.

* logit and probit.

* Report estimates evaluated at the mean of the data.

* Compute standard errors manually.
# 9. Regression Model

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>COB M/F LFP 15-65</td><td>0.50***<br>[0.05]</td><td>0.51***<br>[0.06]</td><td>0.51***<br>[0.06]</td></tr><tr><td>Model</td><td>OLS</td><td>Logit</td><td>Probit</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>
* Cultural Proxy: female to male LFP 15-65, ILO EST, 1990-2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
# Standard Regression Table

<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 F/M 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

* (4): ↑ 1pp COB FLFP associated with ↑ 0.3pp Active.
<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 F/M 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

* (4) ↑ 10pp COB FLFP associated with ↑ 6% of mean in Active.
<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 F/M 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

• (4): ↑ 1 s.d. COB FLFP associated with ↑ 0.53 s.d. in Active.
# Standard Regression Table

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="6">Active</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>COB M/F LFP 15-65</td><td>0.33***<br>[0.06]</td><td>0.31***<br>[0.06]</td><td>0.31***<br>[0.06]</td><td>0.27***<br>[0.04]</td><td>0.21***<br>[0.06]</td><td>1.00<br>[0.82]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls interacted</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>State and year FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual and Spouse</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>COB</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>All</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>COB</td><td>109</td><td>109</td><td>109</td><td>109</td><td>109</td><td>33</td></tr><tr><td>Observations</td><td>268,452</td><td>233,199</td><td>233,199</td><td>233,199</td><td>233,199</td><td>1,046</td></tr><tr><td>Adj. R2</td><td>0.106</td><td>0.104</td><td>0.106</td><td>0.179</td><td>0.179</td><td>0.308</td></tr></tbody></table>

Outcome: mean = 0.59, s.d. = 0.13

COB proxy: mean = 0.69, s.d. = 0.21

* 662 State-year FE; 38,303 individual and spouse FE; 43 COB FE.
① Interpretation of cultural proxy.

② Unobserved human capital.

③ Assimilation.

④ Timing of assignment.

⑤ Institutional environment.

⑥ Referential group: parents, partners, neighborhood.

⑦ Within cultural heterogeneity.

⑧ External validity.

⑨ Sample selection.
# 1. Interpretation of Cultural Proxy

How does cultural proxy compare to COB overall effect?

* Estimate COB overall effects:

* At least 50 obs per COB (100 COB, drop 212 obs, 9 COB).

* Regress outcome on COB FE and individual controls.

* 99 purged COB FE (relative to MEX).

* Regress 100 COB FE on cultural proxy and COB characteristics.

* Average time-varying COB characteristics.
# 1. Interpretation of Cultural Proxy

<table><thead><tr><th rowspan="3"></th><th colspan="2">COB FE</th></tr><tr><th>Unpurged</th><th>Purged</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>MEX</td><td>0.00</td><td>0.00</td></tr><tr><td>IND</td><td>0.07***</td><td>0.02*</td></tr><tr><td>CHN</td><td>0.20***</td><td>0.12***</td></tr><tr><td>PHL</td><td>0.26***</td><td>0.14***</td></tr><tr><td>SLV</td><td>0.16***</td><td>0.14***</td></tr><tr><td>KOR</td><td>-0.07***</td><td>-0.15***</td></tr><tr><td>JPN</td><td>-0.07***</td><td>-0.14***</td></tr><tr><td>GTM</td><td>0.04***</td><td>0.03***</td></tr><tr><td>CAN</td><td>0.18***</td><td>0.13***</td></tr><tr><td>VNM</td><td>0.22***</td><td>0.18***</td></tr></tbody></table>

* MEX male to female LFP 15-65 = 0.47 (among lowest).

* MEX % active = 0.44.
# 1. Interpretation of Cultural Proxy

<table><thead><tr><th rowspan="3">Dependent variable:</th><th colspan="6">COB FE</th></tr><tr><th colspan="3">Unpurged</th><th colspan="3">Purged</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>COB M/F LFP 15-65</td><td>0.36***<br>[0.05]</td><td>0.34***<br>[0.06]</td><td>0.28***<br>[0.04]</td><td>0.28***<br>[0.05]</td><td></td><td></td></tr><tr><td>COB controls</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Adj. R2</td><td>0.432</td><td>0.308</td><td>0.575</td><td>0.369</td><td>0.280</td><td>0.529</td></tr></tbody></table>

* COB proxy alone captures large part of COB overall effect on FLFP.

* COB controls do not mediate this relationship.
# 1. Interpretation of Cultural Proxy

Does cultural proxy captures role of cultural change?

* Include COB FE.

* Control for years since in U.S. rather than immigration year FE.

* Identification: within COB time variation in cultural proxy.

* Very demanding on data.
# 1. Interpretation of Cultural Proxy

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>COB F/M LFP 15-65</td><td>0.31***<br>[0.05]</td><td>0.35***<br>[0.06]</td><td>0.25*<br>[0.13]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>COB FE</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>COB proxy variance</td><td>0.0304</td><td>0.0304</td><td>0.0304</td></tr><tr><td>COB proxy residual variance</td><td>0.0284</td><td>0.0039</td><td>0.0005</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Very demanding on data: little residual variance left for identification.

* 1990-2017: $\Delta$ COB F/M LFP 15-65 $0.48 \Rightarrow 0.66$.

* $\Delta$ COB F/M LFP 15-2017 associated with $\uparrow$ 0.09 s.d. in active.
# 2. Unobserved Human Capital

* COB proxy could capture unobservable heterogeneity in HK.

* Social capital might vary with COB and correlate with proxy.

* Control for parental education. Fernández and Fogli (2009)

* Control for pre-migration characteristics. Blau and Kahn (2015)

* If unobservables, reflected in market wage.

* Use Mincer equation.

* Compute log hourly wage rates.

* OLS and Heckman selection model.

* Selection equation: number of children and spouse's characteristics.
## 2. Unobserved Human Capital

<table><thead><tr><th rowspan="3">Dependent variable:<br>Estimator:</th><th colspan="4">Log hourly wage</th></tr><tr><th colspan="2">OLS</th><th colspan="2">Heckit</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>0.56<br>[0.46]</td><td>0.20**<br>[0.08]</td><td>0.35<br>[0.40]</td><td>0.21***<br>[0.06]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Hourly wage mean</td><td>23.23</td><td>23.23</td><td>23.23</td><td>23.23</td></tr><tr><td>Hourly wage s.d.</td><td>7.60</td><td>7.60</td><td>7.60</td><td>7.60</td></tr><tr><td>Observations</td><td>134,262</td><td>134,262</td><td>268,455</td><td>268,455</td></tr><tr><td>Censored observations</td><td></td><td></td><td>134, 193</td><td>134, 193</td></tr></tbody></table>

COB measure: mean = 0.69, s.d. = 0.21

* Magnitude in (4):

* ↑ 1 s.d. COB M/F LFP 15-65 associated with ↑ 4.41% hourly wage.

* At mean, hourly wage ↑ from $23.23 to $24.25.

* Corresponds to ↑ 0.13 s.d. in hourly wage.
3. Assimilation

* COB culture might dissipate over time.

* Blau, Kahn, and Papps (2011) analyze convergence to native levels.

* See also Neuman (2018).

* Include interaction with *years since in the U.S.*

* Might depend on cultural trait.
3. Assimilation

* Cultural Proxy: F/M LFP 15-65, ILO EST, 1990-2017.

* Controls: individual, spouse, COB.

* No FE for year of immigration.

* FE for year of birth.

* Outcome: indicator for *In the labor force*.

* Timing of assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
## 3. Assimilation

<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>0.29***<br>[0.08]</td><td></td><td>0.35***<br>[0.06]</td><td>0.26***<br>[0.09]</td></tr><tr><td>Years since in U.S.</td><td></td><td>0.01***<br>[0.00]</td><td>0.01***<br>[0.00]</td><td>0.00<br>[0.00]</td></tr><tr><td>Interaction</td><td></td><td></td><td></td><td>0.01*<br>[0.01]</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>

Years since the U.S: mean = 10.21, s.d. = 6.10

* (Some) evidence of assimilation over time.

* Assimilation driven by migrants from progressive countries?

* Explore further (e.g., critical age).
4.a. Assignment: Year vs Decade Average

* Cultural Proxy: M to F LFP 15-65, ILO Estimates (EST), 1990-2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* COB characteristics assignment: year vs decade of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
4.a. Assignment: Year vs Decade Average

<table><thead><tr><th rowspan="3">Dependent variable:<br>COB assignment:</th><th colspan="4">Active</th></tr><tr><th colspan="2">Year</th><th colspan="2">Decade</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>0.33***<br>[0.06]</td><td>0.31***<br>[0.05]</td><td>0.35***<br>[0.06]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.69</td><td>0.70</td><td>0.70</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>

* Practically same results.

* Year enables more time variation.
4.b. Assignment: Year of Migration vs Current

* Cultural Proxy: M to F LFP 15-65, ILO Estimates (EST), 1990-2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* COB characteristics assignment: year of migration vs current year.

* Two-way clustering: State + COB.

* Residence location FE: State.
4.b. Assignment: Year of Migration vs Current

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB M/F LFP 15-65 (at migration)</td><td>0.31***<br>[0.05]</td><td>0.33***<br>[0.06]</td><td></td><td></td><td>0.34***<br>[0.12]</td></tr><tr><td>COB M/F LFP 15-65 (current)</td><td></td><td></td><td>0.32***<br>[0.08]</td><td>0.27***<br>[0.07]</td><td>-0.05<br>[0.14]</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td><td>268,452</td></tr></tbody></table>

COB measure (at migration): mean = 0.69, s.d. = 0.21

COB measure (current): mean = 0.72, s.d. = 0.20

* Straightforward: past rather than current measure matters.

* Little evidence of cultural links after migration.
4.b. Assignment: Migration vs Age 15 vs Current

<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 (age 15)</td><td>0.28***<br/>[0.06]</td><td></td><td></td><td>-0.10<br/>[0.09]</td></tr><tr><td>COB M/F LFP 15-65 (at migration)</td><td></td><td>0.39***<br/>[0.06]</td><td></td><td>0.57***<br/>[0.15]</td></tr><tr><td>COB M/F LFP 15-65 (current)</td><td></td><td></td><td>0.29***<br/>[0.07]</td><td>-0.16<br/>[0.16]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>114,413</td><td>114,413</td><td>114,413</td><td>114,413</td></tr></tbody></table>

COB measure (age 15): mean = 0.66, s.d. = 0.22

COB measure (at migration): mean = 0.70, s.d. = 0.21

COB measure (current): mean = 0.73, s.d. = 0.21

* Preference formation relevant throughout adulthood.
# 5. Institutional Environment

* Approach requires fixing institutional environment.

* What is the relevant level?

* States: 51 categories.

* Counties: 475 identifiable counties, 70k obs not identifiable (21%).

* PUMAs: 3,199 categories but heterogeneous (varies across years).

* Potentially selected migration across local labor markets.
5. Institutional Environment: States vs PUMAs

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.42***<br>[0.07]</td><td>0.42***<br>[0.07]</td><td>0.41***<br>[0.07]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Residence level</td><td>U.S.</td><td>State</td><td>PUMA</td></tr><tr><td>Residence FE</td><td>1</td><td>51</td><td>3,189</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>268,445</td></tr></tbody></table>
# 5. Institutional Environment: States vs Counties

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="5">Active</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>COB FLFP 15+</td><td>0.42***<br>[0.07]</td><td>0.43***<br>[0.08]</td><td>0.43***<br>[0.08]</td><td>0.42***<br>[0.07]</td><td>0.42***<br>[0.08]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Residence level</td><td>U.S.</td><td>U.S.</td><td>State</td><td>PUMA</td><td>County</td></tr><tr><td>Residence FE</td><td>1</td><td>1</td><td>51</td><td>3,189</td><td>474</td></tr><tr><td>Observations</td><td>268,452</td><td>211,593</td><td>211,593</td><td>211,589</td><td>211,593</td></tr></tbody></table>
# 6. Referential Group

Implicit model of cultural transmission, with a relevant referential.

* Individuals themselves (1st generation immigrants).

* Parents through vertical transmission.

* Spouse through within-marriage transmission / selection.

* Neighborhood through oblique socialization.
* Directly, 2nd generation immigrants.
  Fernández and Fogli (2009) Blau, Kahn, Liu, and Papps (2013)

* 3rd generation immigrants.
  Costa-Font, Giuliano and Ozcan (2018) (savings in UK)

* Rough approximation: “1.5th generation”, arrived young.
* Fernández and Fogli (2009)

* U.S. ACS 1970 1% Metro (contains father country of birth).

* Married women 30–40, father born outside U.S.

* Assign FLFP 10+ of father COB in 1950 using ILO YI.

* 7k women over 25 countries of ancestry.

* Outcome: Hours worked in previous week.

* ↑ 1 s.d. FLFP COB ⇒ with ↑ 0.82 hours, or 0.3 s.d. across COA.
6.a. Referential Group: Parents

• Fernández and Fogli (2009) Table 2

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Hours worked</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>FCOB FLFP 10+ 1950</td><td>0.047***<br>[0.012]</td><td>0.041**<br>[0.016]</td><td>0.072***<br>[0.015]</td><td>0.045***<br>[0.016]</td></tr><tr><td>Education FE</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Husband education FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Husband income</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of children</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>SMSA FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>COB clusters</td><td>25</td><td>25</td><td>25</td><td>25</td></tr><tr><td>Observations</td><td>6,774</td><td>6,774</td><td>6,774</td><td>6,774</td></tr><tr><td>Adj. R2</td><td>0.018</td><td>0.024</td><td>0.053</td><td>0.053</td></tr></tbody></table>
6.a. Referential Group: Parents

• Blau, Kahn, Liu, and Papps 2013

• U.S. March CPS 1995–2011 (contains parents country of birth).

• Married women 25–49, father or mother born outside U.S.

• Assign hours worked of immigrants in ACS 1970–2000 at age 10.

• 20k women over 69 countries of ancestry.

• Outcome: annual hours worked.

• Positive effect of mother COB hours.

• Larger for mother COB than father's COB women hours.

• Decomposition by family type.
6.a. Referential Group: Parents (1.5th Generation)

* Cultural Proxy: M to F LFP 15-65, ILO Estimates (EST), 1990-2017.

* Controls: individual, spouse, COB.

* Outcome: indicator for *In the labor force*.

* COB characteristics assignment: year of migration.

* Two-way clustering: State + COB.

* Residence location FE: State.
6.a. Referential Group: Parents (1.5th Generation)

<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>0.30***<br>[0.06]</td><td>-0.02<br>[0.06]</td><td>0.26***<br>[0.06]</td><td>0.27***<br>[0.06]</td></tr><tr><td>Age migrated</td><td>All</td><td>&lt; 10</td><td>&lt; 15</td><td>&lt; 20</td></tr><tr><td>Outcome mean</td><td>0.64</td><td>0.71</td><td>0.72</td><td>0.70</td></tr><tr><td>Outcome s.d.</td><td>0.12</td><td>0.28</td><td>0.19</td><td>0.14</td></tr><tr><td>COB measure mean</td><td>0.69</td><td>0.64</td><td>0.66</td><td>0.67</td></tr><tr><td>COB measure s.d.</td><td>0.21</td><td>0.23</td><td>0.22</td><td>0.21</td></tr><tr><td>Observations</td><td>411,973</td><td>3,375</td><td>16,477</td><td>71,785</td></tr></tbody></table>
6.b. Referential Group: Spouse

* Spouse's culture might matter:

* Assortative matching.

* Household bargaining.

* Sample selection, spouse's side:

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

* No age restriction.

* Born in single identified country.
Compare main results across regression samples.

* Cultural Proxy: wife's M to F LFP 15-65, ILO EST, 1990-2017.

* Controls: individual, spouse, wife's COB.

* Outcome: indicator for *In the labor force*.

* Wife's COB characteristics assignment: wife's year of migration.

* Two-way clustering: State + wife's COB.

* Residence location FE: State.
6.b. Referential Group: Spouse

<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>0.33***<br>[0.06]</td><td>0.29***<br>[0.08]</td><td>0.36***<br>[0.06]</td></tr><tr><th>Sample</th><th>Original</th><th>Original</th><th>Restricted</th><th>Restricted</th></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Outcome mean</td><td>0.52</td><td>0.52</td><td>0.51</td><td>0.51</td></tr><tr><td>Outcome s.d.</td><td>0.50</td><td>0.50</td><td>0.50</td><td>0.50</td></tr><tr><td>COB measure mean</td><td>0.59</td><td>0.59</td><td>0.58</td><td>0.58</td></tr><tr><td>COB measure s.d.</td><td>0.17</td><td>0.17</td><td>0.18</td><td>0.18</td></tr><tr><td>Observations</td><td>268,452</td><td>268,452</td><td>172,262</td><td>172,262</td></tr></tbody></table>
6.b. Referential Group: Spouse

His or her culture?

* Isolate each spouse's culture.

* Include spouse's COB FE.

* Include spouse's COB × year immigration FE.

* Compare spouses' cultures directly.

* Include both spouses' cultures.

* Differentiate spouses from same COB and different COB.
6.b. Referential Group: Isolate Spouses' Culture

* Cultural Proxies: spouses' COB M to F LFP 15–65, ILO EST, 1990–2017.

* Controls: individual, spouse, wife's COB or husband's COB.

* Spouse's COB FE or COB × year immigration FE.

* Outcome: indicator for wife *In the labor force*.

* Spouse COB characteristics assignment: spouse's year of migration.

* Two-way clustering: State + spouse's COB.

* Residence location FE: State.
6.b. Referential Group: Isolate Spouses' Culture

<table><thead><tr><th rowspan="3">Dependent variable:</th><th colspan="6">Active</th></tr><tr><th colspan="3">Wife's COB culture</th><th colspan="3">Husband's COB culture</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>COB M/F LFP 15-65</td><td>0.36***<br>[0.06]</td><td>0.24***<br>[0.05]</td><td>0.27***<br>[0.04]</td><td>0.41***<br>[0.05]</td><td>0.25***<br>[0.04]</td><td>0.21***<br>[0.04]</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Spouse COB</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Spouse COB FE</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Spouse COB × YI FE</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>COB proxy variance</td><td>0.0293</td><td>0.0293</td><td>0.0293</td><td>0.0300</td><td>0.0300</td><td>0.0300</td></tr><tr><td>COB proxy residual variance</td><td>0.0040</td><td>0.0015</td><td>0.0015</td><td>0.0056</td><td>0.0015</td><td>0.0018</td></tr><tr><td>Observations</td><td>149,737</td><td>149,737</td><td>149,737</td><td>149,737</td><td>149,737</td><td>149,737</td></tr></tbody></table>

Outcome: mean = 0.54, s.d. = 0.14

COB proxy: mean = 0.69, s.d. = 0.19

* Share of couples same COB: 92%.

* Share of couples same COB and same year immigration: 48%.
6.b. Referential Group: Isolate Spouses' Culture

<table><thead><tr><th rowspan="3">Dependent variable:</th><th colspan="6">Active</th></tr><tr><th colspan="3">Wife's COB culture</th><th colspan="3">Husband's COB culture</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>COB M/F LFP 15-65</td><td>0.36***<br>[0.06]</td><td>0.24***<br>[0.05]</td><td>0.27***<br>[0.04]</td><td>0.41***<br>[0.05]</td><td>0.25***<br>[0.04]</td><td>0.21***<br>[0.04]</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Spouse COB</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Spouse COB FE</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Spouse COB × YI FE</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>COB proxy variance</td><td>0.0293</td><td>0.0293</td><td>0.0293</td><td>0.0300</td><td>0.0300</td><td>0.0300</td></tr><tr><td>COB proxy residual variance</td><td>0.0040</td><td>0.0015</td><td>0.0015</td><td>0.0056</td><td>0.0015</td><td>0.0018</td></tr><tr><td>Observations</td><td>149,737</td><td>149,737</td><td>149,737</td><td>149,737</td><td>149,737</td><td>149,737</td></tr></tbody></table>

Outcome: mean = 0.54, s.d. = 0.14

COB proxy: mean = 0.69, s.d. = 0.19

* (3): ↑ 1 s.d. in wife's COB proxy ⇒ ↑ 0.37 s.d. in outcome.

* (6): ↑ 1 s.d. in husband's COB proxy ⇒ ↑ 0.14 s.d. in outcome.
6.b. Referential Group: Compare Spouses' Culture

Compare spouses' cultures directly

* Cultural Proxies: spouses' COB M to F LFP 15–65, ILO EST, 1990–2017.

* Controls: individual, spouse, wife's COB and husband's COB.

* Differentiate if same COB.

* Outcome: indicator for wife *In the labor force*.

* Spouse COB characteristics assignment: spouse's year of migration.

* Three-way clustering: State + spouse's COB.

* Residence location FE: State.
6.b. Referential Group: Compare Spouses' Culture

<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>Same × COB M/F LFP 15-65</td><td>0.37***<br>[0.06]</td><td>0.35***<br>[0.07]</td><td>0.39***<br>[0.07]</td><td>0.40***<br>[0.07]</td></tr><tr><td>Different × wife's COB M/F LFP 15-65</td><td>0.36***<br>[0.06]</td><td></td><td>0.19***<br>[0.04]</td><td>0.24***<br>[0.04]</td></tr><tr><td>Different × husband's COB M/F LFP 15-65</td><td></td><td>0.36***<br>[0.07]</td><td>0.22***<br>[0.06]</td><td>0.29***<br>[0.07]</td></tr><tr><td>Different × Interaction</td><td></td><td></td><td></td><td>-0.17***<br>[0.05]</td></tr><tr><td>Controls</td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Wife's COB</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Husband's COB</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>169,531</td><td>169,531</td><td>169,531</td><td>169,531</td></tr></tbody></table>
6.b. Referential Group: Spouse

Impact of migrant husbands' culture on US wives?

* Born in the U.S. from US parents.

* Age: 30–49.

* Regular household, no farms, married spouse present.

* Husband born from non US parents.

* Husband born in single identified country outside U.S.

* Husband migrated after 1990.
6.b. Referential Group: Spouse

Impact of migrant husbands' culture on US wives?

* Cultural Proxy: husband COB M to F LFP 15–65, ILO EST, 1990–2017.

* Controls: individual, spouse, husband's COB.

* Outcome: indicator for wife *In the labor force*.

* Spouse COB characteristics assignment: spouse's year of migration.

* Two-way clustering: State + spouse's COB.

* Residence location FE: State.
6.b. Referential Group: Spouse

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Active</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>Husband's COB M/F LFP 15-65</td><td>0.16***<br>[0.03]</td><td>0.08**<br>[0.03]</td></tr><tr><td>Controls</td><td></td><td></td></tr><tr><td>Individual</td><td>Yes</td><td>Yes</td></tr><tr><td>Husband's COB</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>51,019</td><td>51,019</td></tr></tbody></table>

COB measure: mean = 0.68 ; s.d. = 0.22

Outcome: mean = 0.77 ; s.d. = 0.10

• (2): ↑ s.d. in husband's COB proxy = ⇒ ↑ 0.18 s.d. in outcome.
6.c. Referential Group: Neighborhood

Role of neighborhood through oblique socialization?

* Migrants clustering might preserve and transmit COB culture.

* Mechanism: role models or rewards/punishment.

* Compute COB immigrants density + interact COB cultural.

* Three levels of aggregation (data limitations):

* States-year: 51 categories.

* PUMAs-year: 3,199 categories but heterogeneous definition.

* Counties-year: 475 identifiable counties.

* Two reference groups:

* General population.

* Married women aged 30–49.
6.c. Referential Group: Neighborhood

<table><thead><tr><th rowspan="3">Dependent variable:<br>Reference population:</th><th colspan="4">Active</th></tr><tr><th colspan="2">General</th><th colspan="2">Women 30-49</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>0.33***<br>[0.06]</td><td>0.32***<br>[0.76]</td><td>0.33***<br>[0.06]</td><td>0.32***<br>[0.06]</td></tr><tr><td>COB density</td><td></td><td>-1.60**<br>[0.76]</td><td></td><td>-0.93**<br>[0.45]</td></tr><tr><td>Interaction</td><td></td><td>2.92**<br>[1.44]</td><td></td><td>1.62*<br>[0.83]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Aggregation</td><td>State</td><td>State</td><td>State</td><td>State</td></tr><tr><td>Units</td><td>51</td><td>51</td><td>51</td><td>51</td></tr><tr><td>Observations</td><td>268,451</td><td>268,451</td><td>268,451</td><td>268,451</td></tr></tbody></table>
6.c. Referential Group: Neighborhood

<table><thead><tr><th rowspan="3">Dependent variable:<br>Reference population:</th><th colspan="4">Active</th></tr><tr><th colspan="2">General</th><th colspan="2">Women 30-49</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>0.32***<br>[0.05]</td><td>0.30***<br>[0.24]</td><td>0.32***<br>[0.05]</td><td>0.30***<br>[0.05]</td></tr><tr><td>COB density</td><td></td><td>-0.51**<br>[0.24]</td><td></td><td>-0.35***<br>[0.12]</td></tr><tr><td>Interaction</td><td></td><td>1.01**<br>[0.44]</td><td></td><td>0.61***<br>[0.23]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Aggregation</td><td>PUMA</td><td>PUMA</td><td>PUMA</td><td>PUMA</td></tr><tr><td>Units</td><td>3,189</td><td>3,189</td><td>3,189</td><td>3,189</td></tr><tr><td>Observations</td><td>268,444</td><td>268,444</td><td>268,443</td><td>268,443</td></tr></tbody></table>
6.c. Referential Group: Neighborhood

<table><thead><tr><th rowspan="3">Dependent variable:<br>Reference population:</th><th colspan="4">Active</th></tr><tr><th colspan="2">General</th><th colspan="2">Women 30-49</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>0.33***<br>[0.06]</td><td>0.31***<br>[0.48]</td><td>0.33***<br>[0.06]</td><td>0.31***<br>[0.06]</td></tr><tr><td>COB density</td><td></td><td>-0.86*<br>[0.48]</td><td></td><td>-0.53**<br>[0.23]</td></tr><tr><td>Interaction</td><td></td><td>1.81**<br>[0.90]</td><td></td><td>1.03**<br>[0.44]</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Aggregation</td><td>County</td><td>County</td><td>County</td><td>County</td></tr><tr><td>Units</td><td>474</td><td>474</td><td>474</td><td>474</td></tr><tr><td>Observations</td><td>211,592</td><td>211,592</td><td>211,592</td><td>211,592</td></tr></tbody></table>
# 6. Referential Group: Mechanisms to Explore

* 1st generation immigrants.

* 1.5th generation immigrants (vertical transmission).

* 2nd generation: mothers, fathers (vertical transmission).

* 1st generation: partner (transmission through marriage).

* 2nd generation: parents in-law (transmission through marriage).

* Social spillovers within neighborhoods (oblique transmission).
# 7. Within Culture Heterogeneity

* What about within-group cultural heterogeneity?

* Solution:

* Cultural proxy by sub-group e.g. COB FLFP 30-25, married, college +.

* E.g. use IPUMS-I (~300M observations across ~100 countries)

* Enables using within-COB variation ⇒ improves identification.

* Alleviates OVB on origin side.

* Credible that environment is fixed (rules + networks).

* E.g. Marcén and Morales (2018, 2019) Marcén et al. (2018)

* Estimates increase in magnitude and precision.

* But not possible/relevant depending on question.
# 8. External Validity

* In this context: use effective sample approach.

* Similar results across time (migration waves)?

* Similar results across host environments (countries)?

* Dependent upon culture and cultural trait (assimilation, identity).

* Replicate across time and space.

* Meta-analysis or single analysis Luttmer and Singhal (2011)
  Rodríguez-Planas and Nollenberger (2018)
  Beblo, Görges, and Markowsky (2018) Hoorn (2019)
# 9. Selection

* Are immigrants representative of COB?

* Compare characteristics.

* Pre-immigration data. Blau and Kahn (2015)

* Selective in-migration.

* Self-selection into more dynamic local labor markets.

* Compare within local labor market.

* Selective out-migration.

* Depends on context.

* Here: tied movers, so less likely.

* Generally: does cultural proxy predicts migration?

* Unlikely to drive pattern in data.
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?

* Specific (and standard) empirical issues still apply.
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

* Explain contemporaneous distribution of trust in Africa: the slave trade. Nunn and Wantchekon (2011)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

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

* Explain contemporaneous distribution of economic status in Africa: ancestral lifeways. Michalopoulos, Putterman and Weil (2019)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

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

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

* Mechanisms of persistence of historical shocks: contemporaneous gender norms in Africa and the slave trade. Teso (2019)
* Explain contemporaneous distribution of norms about gender roles: pre-industrial division of labor. Alesina, Giuliano, and Nunn (2013)

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

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

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

* Mechanisms of persistence of historical shocks: contemporaneous and past gender norms in France and WWI. Gay (2023)
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_123.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></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr><tr><th colspan="8">Dependent variable: Traditional participation of females relative to males in the following tasks:</th></tr><tr><th></th><th>Overall agriculture</th><th>Land clearance</th><th>Soil preparation</th><th>Planting</th><th>Crop tending</th><th>Harvesting</th><th></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)</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><tr><th></th><th>Caring for small animals</th><th>Caring for large animals</th><th>Milking</th><th>Cooking</th><th>Fuel gathering</th><th>Water fetching</th><th>Burden carrying</th><th>Handicrafts</th><th>Trading</th></tr></thead><tbody><tr><th>Mean of dep. var.</th><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><th>Traditional plough use</th><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><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><td>yes</td><td>yes</td></tr><tr><th>Observations</th><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><th>Adjusted R-squared</th><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><th>R-squared</th><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_124.png" /></figure>
## Traditional plough use among ethnic groups

<figure><img src="image_125.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_126.png" /><figcaption>Partial Correlation: Plough and FLFP</figcaption></figure>
<figure><img src="image_127.png" /><figcaption>Partial Correlation: Plough and Ownership</figcaption></figure>
Partial Correlation: Plough and Politics

<figure><img src="image_128.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 colspan="3">(4)</th><th colspan="2">(5)</th><th colspan="2">(6)</th></tr><tr><th colspan="3">Dependent variable:</th><th colspan="2">Men better political leaders, 1995–2007</th></tr><tr><th></th><th>Female labor force participation, 1995–2007</th><th></th><th></th><th>When jobs are scarce, 1995–2007</th><th></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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><td></td><td></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 identity 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).
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>
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><caption>Dependent variable (panels B & C):</caption><thead><tr><th></th><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" style="text-align: center;">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>no</td><td>yes</td><td>no</td><td>yes</td><td>yes</td><td>yes</td><td>no</td><td>yes</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></th><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_129.png" /><figcaption>Transatlantic slave trade</figcaption></figure>
<figure><img src="image_130.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).

* $\mathbf{X}'_{i,e,d,c}$: 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.
# Causality

* 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>
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>Trust of local council<br>(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_131.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>0.1919***<br>(0.0357)</td><td>0.1079***<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 liefeway 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>
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_132.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.
Ethnic group-level exposure to transatlantic slave trade

<figure><img src="image_133.png" /></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_134.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>
# The Intergenerational Transmission of World War I on Female Labour

Victor Gay*

March 2023

Forthcoming at *The Economic Journal*
* What are the mechanisms of intergenerational transmission?

* What is the process of cultural change?
* What are the mechanisms of intergenerational transmission?

* What is the process of cultural change?

* Use historical shock to FLFP: WWI in France.

* Cultural proxy: WWI military fatalities (⇒ interwar ↑ FLFP)

* Outcomes: FLFP and attitudes toward gender roles.

* Hypotheses:

* Parental transmission Fernández (2013)

* Transmission through marriage
    Fernández, Fogli and Olivetti (2004)

* Oblique and horizontal social spillovers
    Fernández (2013) Fogli and Veldkamp (2011)
# WWI Military Fatalities: Context

* WWI: 1914–1918, mostly in North-Eastern France.

* Universal conscription system (men aged 20–48):

* 8 million drafted out of 10 million men aged 15–50 in 1914.

* 1.3 million died in combats → death rate of 16%.
# WWI Military Fatalities: Impact on Sex Ratio

<figure><img src="image_135.png" /><figcaption>Adult Sex Ratio (1901–2012)</figcaption></figure>
# WWI Military Fatalities: Impact on Sex Ratio

## Difference-in-Differences Estimates

<figure><img src="image_136.png" /></figure>
# WWI Military Fatalities: Data

• Original dataset:

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

• Information: département of birth, dates of birth and death.
# WWI Military Fatalities: Data

* Original dataset:

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

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

* Measure of military death rates at the département level:

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

* Summary statistics:

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

* Minimum = 6%, maximum = 29%.
# WWI Military Fatalities: Distribution

<figure><img src="image_137.png" /><figcaption>Military Death Rates Across 87 Départements</figcaption></figure>
* Until end 1914: territorial organization of military recruitment.

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

* Pre-war characteristics:

* Correlated with measures of rurality.

* Uncorrelated with other pre-war characteristics.

* Uncorrelated with pre-war levels and trends in FLFP.
* Main results. Boehnke and Gay (2022)

* Data: 7 censuses 1901–1936, département level.

* Empirical strategy: difference-in-differences.

* Estimate: 20% vs 10% death rates $\Rightarrow$ ↑ 4pp.

* Magnitude: $\sim$ 12% of pre-war FLFP mean (0.4 s.d.).
## Main results. Boehnke and Gay (2022)

* Data: 7 censuses 1901–1936, département level.

* Empirical strategy: difference-in-differences.

* Estimate: 20% vs 10% death rates $\Rightarrow$ ↑ 4pp.

* Magnitude: ~ 12% of pre-war FLFP mean (0.4 s.d.).

## Mechanisms.

* Evidence for labor supply channels (war widows, single women).

* Little evidence for labor demand channels (substitution).

* Little role of female wartime employment.
Military Fatalities and FLFP: Interwar Period

<figure><img src="image_138.png" /><figcaption>Interwar impact of WWI military fatalities on FLFP</figcaption></figure>
* Data: 8 micro census 1962–2012 (20–25% samples).

* Sample: **internal migrant** French married women aged 30–49 (3.5 million observations).

* Empirical strategy: repeated cross-sections

$$ \text{employed}_{ibrt} = \beta \text{ death\_rate}_b + \alpha \mathbf{X}'_{1911,b} + \gamma_i \cdot \delta_r \cdot \mu_{1914,b} + \varepsilon_{ibrt} $$

* $i$ = individual, $b$ = dép. of birth, $r$ = dép. of residence, $t$ = census.

* $\mathbf{X}'_{1911,b}$: pre-war controls (rurality, determinants of FLFP).

* $\gamma_i$: year of birth FE.

* $\delta_r$: **département** of residence FE (~ 90 dép).

* $\mu_{1914,b}$: region FE (~ 4–5 dép per region).

* Two-way clustering: **département** of birth and of residence.
Military Fatalities and FLFP: Long Run

<figure><img src="image_139.png" /></figure>

* Born in dép 20% vs 10% death rates $\Rightarrow$ 4–6 pp more likely to work.
Military Fatalities and FLFP: Long Run

<figure><img src="image_140.png" /></figure>
* Strategy 1: location-based approach.

* Sample: non-migrant French married women aged 30–49.

* Identification: across locations.

* Capture culture and location effects.

$$ \text{employed}_{irt} = \beta \text{ death\_rate}_r + \alpha \mathbf{X}'_{1911,r} + \gamma_i \cdot \mu_{1914,r} + \varepsilon_{irt} $$
* Strategy 1: location-based approach.

* Sample: non-migrant French married women aged 30-49.

* Identification: across locations.

* Capture culture and location effects.

$$ \text{employed}_{irt} = \beta \ \text{death\_rate}_r + \alpha \mathbf{X}'_{1911,r} + \gamma_i \cdot \mu_{1914,r} + \varepsilon_{irt} $$

* Strategy 2: inverted epidemiological.

* Sample: migrant French married women aged 30-49.

* Identification: across locations within origins.

* Capture location effects independent of culture.

$$ \text{employed}_{ibrt} = \beta \ \text{death\_rate}_r + \alpha \mathbf{X}'_{1911,r} + \gamma_i \cdot \omega_b \cdot \mu_{1914,r} + \varepsilon_{ibrt} $$
Epidemiological versus Location

<figure><img src="image_141.png" /></figure>

• Cultural transmission ≈ 43–62% of total effect.
<figure><img src="image_142.png" /></figure>

• Cultural transmission ≈ 39–60% of total effect.
## Transmission on behavior side:

* Parental transmission: mothers and fathers to daughters.

* Transmission through marriage:

* Husbands to wives.

* Mothers in-law to daughters in-law.

* Local social interactions.
* Mother-to-daughter channel.

* Data: extended labor surveys 2005–2012 (contain parental origins).

* Sample: second-generation internal migrant married women.

* Second-generation internal migrant = non-migrant with both parents born in another département than daughter.
* Specification:

$$ \text{employed}_{imfr} = \beta \text{ death\_rate}_m + \alpha \mathbf{X}'_{1911,m} + \gamma_i + \delta_r + \mu_{1914,m} + \omega_f + \varepsilon_{imfr} $$

* $i$ = individual, $m$ = mother, $f$ = father.

* $\tilde{\mathbf{X}}'_{1911,m}$: mother pre-war controls.

* $\gamma_i$: cohort FE.

* $\delta_r$: dép of residence FE.

* $\mu_{1914,m}$: mother region FE.

* $\omega_f$: father birth dép FE.

* Two-way clustering: département of residence and mothers' birth.
Identify $\beta$ from variations in the working behavior of second-generation internal migrant women:

* residing in the same département

* born in the same département

* whose fathers were born in the same département

* but whose mothers were born in neighboring départements
Vertical Transmission: Results

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Employed</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Military death rate, mother</td><td>1.05***<br>[0.37]</td><td>1.11***<br>[0.40]</td><td>1.24***<br>[0.35]</td></tr><tr><td>Pre-war controls, mother (1911)</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Birth-residence dép. FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Mother birth region FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Father birth dép. FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual controls</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Household controls</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>15,095</td><td>15,095</td><td>15,095</td></tr></tbody></table>

<footnote>*** Significant at the 1 percent level.</footnote>
Vertical Transmission: Mothers vs Fathers

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Employed</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Same × Military death rate (parents)</td><td>1.19***<br>[0.42]</td><td>1.08*<br>[0.60]</td><td>1.09*<br>[0.60]</td></tr><tr><td>Different × Military death rate (mother)</td><td>0.73*<br>[0.43]</td><td>0.86*<br>[0.47]</td><td>0.94**<br>[0.45]</td></tr><tr><td>Different × Military death rate (father)</td><td>0.46<br>[0.34]</td><td>0.13<br>[0.60]</td><td>0.07<br>[0.60]</td></tr><tr><td>Parents birth region FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Other controls</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>15,095</td><td>15,095</td><td>15,095</td></tr></tbody></table>

Includes pre-war controls (parents), birth-residence dép. FE, parents birth region FE.
<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="2">Employed</th></tr><tr><th>FS</th><th>SS</th></tr></thead><tbody><tr><td>Military death rate (mother)</td><td>1.94***<br>[0.64]</td><td></td></tr><tr><td>Mother worked</td><td></td><td>0.49**<br>[0.23]</td></tr><tr><td>Pre-war controls, mother (1911)</td><td>Yes</td><td>Yes</td></tr><tr><td>Birth-residence dép. FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Mother birth region FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Father birth dép. FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>15,095</td><td>15,095</td></tr></tbody></table>

F-stat: 44.4
* Husband to wife channel.

* Data: censuses 1962–2012.

* Sample: all women aged 30–49 married with internal migrant man.

$$ \text{employed}_{ihbrt} = \beta \text{ death\_rate}_h + \alpha \mathbf{X}'_{1911,h} + \gamma_i \cdot \delta_r \cdot \omega_b + \varepsilon_{ihbrt} $$

* $i$ = individual, $h$ = husband.

* $\tilde{\mathbf{X}}'_{1911,h}$: husband pre-war controls.

* $\gamma_i$: cohort FE.

* $\delta_r$: dép of residence FE.

* $\omega_b$: birth dép FE.
Without wives birth département FE

<figure><img src="image_143.png" /></figure>

* Husband born in dép 20% vs 10% death rates ⇒ 4-7 pp more likely to work.
With wives birth département FE

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

* Estimates ↓ by 11–41%.
* Assortative matching? A little.

* Working women = valuable trait? Probably.

* Household tasks sharing? Not the case.
* Mother-in-law to daughter channel.

* Data: extended labor surveys 2005–2012 (contain parental origins).

* Sample: second-generation internal migrant married women.

* Second-generation internal migrant = non-migrant with parents born in another département than daughter.

* Same strategy as before but:

* Assign key variables to mother-in-law département of birth.

* Add own parents département of birth FE.
Transmission Through Marriage

<table><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="3">Employed</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Military death rate, mother-in-law</td><td>1.28*<br>[0.75]</td><td>1.48**<br>[0.67]</td><td>1.64**<br>[0.66]</td></tr><tr><td>Pre-war controls, mother-in-law (1911)</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Birth-residence dép. FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Mother-in-law birth region FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Parents birth dép. FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual controls</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Household controls</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>15,095</td><td>15,095</td><td>15,095</td></tr></tbody></table>

<footnote>** Significant at the 5 percent level.</footnote>

<footnote>* Significant at the 10 percent level.</footnote>
* Cultural assimilation at the local (municipality) level.

* Cultural composition in military death rates:

$$ death\_rate_m = \sum_{o} sh\_res_{o,m} \times death\_rate_o $$

* sh\_res\_o,m: share residents in municipality m born in dép. o.
* First-generation internal migrant married women 30–49.

* Censuses 1968–2012.

* Specification:

$$ \text{employed}_{ibmlt} = \beta \text{ death\_rate}_m + \alpha \mathbf{X}'_{1911,m} + \gamma_i + \delta_l + \omega_b + \varepsilon_{ibmlt} $$

* $\mathbf{X}'_{1911,m}$: pre-war controls.

* $\delta_l$: local labor market FE (cantons).

* $\omega_b$: birth dép. FE.

* Values assigned using previous census.

* Across municipality / within canton variation, same dép. of birth.
Transmission Through Local Social Interactions 1

<figure><img src="image_145.png" /></figure>
* Cultural preservation at the local level.

* Share of residents with the same origin: sh\_res<sub>b,m</sub>

* Same sample.

* Specification:

$$ \text{employed}_{ibmlt} = \beta \text{sh\_res}_{b,m} \times \text{death\_rate}_b + \alpha \text{sh\_res}_{b,m} + \gamma_i + \delta_l + \omega_b + \varepsilon_{ibmlt} $$

* Across municipality / within canton variation, same dép. of birth.
Transmission Through Local Social Interactions 2

<figure><img src="image_146.png" /></figure>
* Data: GSS 2005.

* Sample: men and women, all ages.

* Higher values indicate disagreement with the statement.

<table><thead><tr><th></th><th>Statement</th><th>Men</th><th>Women</th></tr></thead><tbody><tr><td>1</td><td>If a woman earns more than her partner, it is bad for their relationship</td><td>0.80<br>(0.28)</td><td>0.76<br>(0.31)</td></tr><tr><td>2</td><td>Women should not be able to decide how to spend the money they earned without asking their partners</td><td>0.70<br>(0.35)</td><td>0.76<br>(0.33)</td></tr><tr><td>3</td><td>In an economic crisis, men should keep their jobs in priority</td><td>0.72<br>(0.35)</td><td>0.76<br>(0.35)</td></tr><tr><td></td><td>Cultural values index (four-points scale)</td><td>2.22<br>(0.65)</td><td>2.28<br>(0.64)</td></tr><tr><td></td><td>Cultural values index (one-point scale)</td><td>0.74<br>(0.22)</td><td>0.76<br>(0.21)</td></tr><tr><td></td><td>Observations</td><td>1,345</td><td>1,900</td></tr></tbody></table>
• Specification

$$
\begin{align*}
\text{values}_{ibr} &= \beta_1 \text{ death\_rate}_b + \beta_2 \text{ female}_i \\
&\quad + \beta_3 \text{ death\_rate}_b \times \text{female}_i \\
&\quad + \alpha \mathbf{X}'_{1911,b} + \gamma_i + \delta_r + \varepsilon_{ibr}
\end{align*}
$$

• Clustering: département.
# Military Fatalities and Current Attitudes

<table><thead><tr><th rowspan="2">Dependent variable</th><th colspan="3">Gender values index</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Military death rate</td><td>0.95***<br>[0.32]</td><td>0.96***<br>[0.32]</td><td>1.00***<br>[0.33]</td></tr><tr><td>Female</td><td>-0.01<br>[0.03]</td><td>-0.01<br>[0.03]</td><td>-0.01<br>[0.03]</td></tr><tr><td>Individual controls</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Parental controls</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>2,688</td><td>2,688</td><td>2,688</td></tr></tbody></table>

* ↑ 1 s.d. in military death rate → ↑ 0.24 s.d. in index.

* No impact on other dimensions (religion, marriage, family).

<footnote>*** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level.</footnote>
## A. First-Generation Internal Migrants

<table><thead><tr><th rowspan="2">Analysis</th><th rowspan="2">Year</th><th rowspan="2">Δ</th><th colspan="3">Estimate</th></tr><tr><th>Raw</th><th>Standardized</th><th>Residual</th></tr></thead><tbody><tr><td>Overall</td><td>1962–2012</td><td>Birth dép.</td><td>0.48***</td><td>0.046***</td><td>0.011***</td></tr><tr><td>Inverted</td><td>1962–2012</td><td>Res dép.</td><td>0.56**</td><td>0.044**</td><td>0.010**</td></tr><tr><td>Husbands</td><td>1962–2012</td><td>Husb dép.</td><td>0.37***</td><td>0.036***</td><td>0.006***</td></tr><tr><td>Assimilation</td><td>1968–2012</td><td>Res muni.</td><td>1.73***</td><td>0.113***</td><td>0.006***</td></tr><tr><td>Preservation</td><td>1968–2012</td><td>Residence municip.</td><td>1.24***</td><td>0.037***</td><td>0.009***</td></tr><tr><td>Attitudes</td><td>2005</td><td>Birth dép.</td><td>1.04***</td><td>0.196***</td><td>0.066***</td></tr></tbody></table>

## B. Second-Generation Internal Migrants

<table><thead><tr><th rowspan="2">Analysis</th><th rowspan="2">Year</th><th rowspan="2">Δ</th><th colspan="3">Estimate</th></tr><tr><th>Raw</th><th>Standardized</th><th>Residual</th></tr></thead><tbody><tr><td>Mother</td><td>2005–2012</td><td>Mother dép.</td><td>1.10***</td><td>0.120***</td><td>0.030***</td></tr><tr><td>Father</td><td>2005–2012</td><td>Father dép.</td><td>0.51</td><td>0.054</td><td>0.015</td></tr><tr><td>In-law</td><td>2005–2012</td><td>In-law dép.</td><td>1.48**</td><td>0.145**</td><td>0.029**</td></tr></tbody></table>