# Table A9: Heterogeneity in the Complementary Effect of Internet and Roads on Employment: Gender

||(1)|(2)<br>**DH**|(3)<br>**S**|(4)|(5)<br>**LS**|(6)<br>**MS**|(7)<br>|(8)<br>**WBES**|
|---|---|---|---|---|---|---|---|---|
||**Worked **<br>**past **|**during**<br>**year**|**Curr**<br>**wor**|**ently**<br>**king**|**Hours **|**worked**|**Number of f**<br>**at end of **|**ull time employee**<br> **last fscal year**|
||**Male**<br>**Sample**|**Female**<br>**Sample**|**Male**<br>**Sample**|**Female**<br>**Sample**|**Male**<br>**Sample**|**Female**<br>**Sample**|**Male**<br>**Sample**|**Female**<br>**Sample**|
|Submarine internet|0.0589**|0.0964***|0.0393|0.0830**|-0.473**|-0.275*|1.078***|0.131***|
||(0.0209)|(0.0287)|(0.0205)|(0.0275)|(0.240)|(0.150)|(0.327)|(0.0478)|
|Proximity to road (KM)|0.00410***<br>(0.000467)|0.00899***<br>(0.000574)|0.00462***<br>(0.000597)|0.00879***<br>(0.000551)|0.401<br>(0.253)|0.964<br>(0.663)|0.448***<br>(0.0392)|1.004***<br>(0.165)|
|Submarine internet * road|0.0118*<br>(0.00489)|0.0193**<br>(0.00610)|0.00527<br>(0.00459)|0.0163**<br>(0.00582)|4.545**<br>(2.044)|3.713**<br>(1.867)|0.0916***<br>(0.0337)|0.00157<br>(0.00273)|
|N|245289|511046|176813|519604|231770|239412|5882|5043|
|ymean|0.898|0.673|0.856|0.620|1.328|1.267|13.332|5.326|
|r2|0.0161|-0.0155|0.0317|0.0234|-0.600|-0.115|0.0426|0.0278|
|r~~2~~<br>a|0.0159|-0.0156|0.0315|0.0233|-0.600|-0.115|0.0311|0.0141|
|Kleibergen-Paap F-statistic|58.36|83.40|60.52|85.79|9.708|11.54|1.485|0.963|
|_P > F_|0.00000144|0.0000106|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|290.0|558.1|230.5|560.2|476.2|597.1|391.7|321.0|
|Windmeijer F-statistic|15.01|12.11|12.04|7.54|9.71|11.54|14.85|15.99|
|Kleibergen-Paap Wald F-statistic|13.09|19.77|13.41|20.44|2.642|2.629|3000.9|5.997|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|
|Gender|✗|✗|✗|✗|✗|✗|✗|✗|
|Place of residence|✓|✓|✓|✓|✓|✓|✗|✗|
|Education|✓|✓|✓|✓|✓|✓|✗|✗|
|Year FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Country FE<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|✓<br>|
|Grid (20*20) connectivity FE|✓|✓|✓|✓|✓|✓|✓|✓|

_Robust standard errors in parentheses clustered at the grid level. ∗p < ._ 10 _∗∗p < ._ 05 _∗∗∗ p <_ 0 _._ 01 _. We use a strategy that combines difference-in-differences and instrumental variables techniques. The causal effect of Internet is estimated using difference-in-differences, while the effects of the road and its interaction with the Internet are estimated using an instrumental variables approach. Our instruments are: the distance to the hypothetical road, the interaction between the distance to the hypothetical road and an indicator for whether an individual (or a firm) is connected to submarine Internet, lightning, and terrain elevation._ Table A10: Heterogeneity in the Complementary Effect of Internet and Roads on Employment: Education

||(1)|(2)<br>**Worked d**<br>**past y**|(3)<br>**uring**<br>**ear**|(4)|(5)|(6)<br>**Curre**<br>**work**|(7)<br>**ntly**<br>**ing**|(8)|(9)|(10)<br>**Hour**<br>**worke**|(11)<br>**s**<br>**d**|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Not educated**<br>**Sample**|**Low educated**<br>**Sample**<br>**(Primary)**|**Not more**<br>**than primary**<br>**Sample**|**higher**<br>**educated**<br>**Sample**<br>**DH**|**Not educated**<br>**Sample**<br>**S**|**Low educated**<br>**Sample**<br>**(Primary)**|**Not more**<br>**than primary**<br>**Sample**|**higher**<br>**educated**<br>**Sample**|**Not educated**<br>**Sample**|**Low educated**<br>**Sample**<br>**(Primary)**<br>**LSMS**|**Not more**<br>**than primary**<br>**Sample**<br>|**higher**<br>**educated**<br>**Sample**|
|Submarine internet|0.181**|0.0677**|0.106***|0.0629*|0.146**|0.0510*|0.0846**|0.0566*|0.0682|-0.207|-0.0774|-0.561**|
||(0.0599)|(0.0248)|(0.0302)|(0.0279)|(0.0542)|(0.0256)|(0.0288)|(0.0272)|(0.138)|(0.130)|(0.0994)|(0.256)|
|Proximity to road (KM)|0.00649***|0.00939***|0.00770***|0.00701***|0.00619***|0.00985***|0.00749***|0.00784***|2.245|0.393|0.282|0.877***|
||(0.000475)|(0.000963)|(0.000473)|(0.00105)|(0.000468)|(0.00101)|(0.000460)|(0.00115)|(2.124)|(0.475)|(0.789)|(0.243)|
|Submarine internet * road|0.0281**|0.0101*|0.0157**|0.0206*|0.0223*|0.00849|0.0125*|0.0168*|2.870|6.304**|1.863|6.982**|
||(0.00992)|(0.00468)|(0.00537)|(0.00846)|(0.00889)|(0.00474)|(0.00506)|(0.00808)|(2.808)|(2.583)|(1.646)|(3.332)|
|N|295558|196514|492072|264263|278239|180856|459095|237322|160837|91574|252405|161285|
|ymean|0.740|0.769|0.752|0.732|0.671|0.703|0.683|0.675|1.090|1.435|1.907|18.72|
|r2|0.0421|0.0362|0.0461|0.0811|0.0914|0.0572|0.0832|0.0914|0.00360|-0.100|0.00356|-0.585|
|r2<br>~~a~~|0.0419|0.0359|0.0460|0.0809|0.0912|0.0570|0.0831|0.0912|0.00351|-0.100|0.00348|-0.585|
|Kleibergen-Paap F-statistic|73.58|51.84|75.90|56.00|73.87|53.23|77.11|53.32|3.161|10.71|9.055|9.315|
|_P > F_|0|0|0|0|0|0|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|213.5|345.9|595.0|228.5|204.3|323.8|561.9|205.6|235.0|436.4|704.0|425.0|
|Windmeijer F-statistic|9.56|12.63|15.50|6.43|8.31|6.94|9.31|3.80|10.16|10.71|9.05|9.32|
|Kleibergen-Paap Wald F-statistic|17.43|11.21|17.94|11.90|17.56|11.58|18.28|11.25|0.491|1.662|1.612|3.061|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Gender|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Place of residence|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Education|✗|✗|✗|✗|✗|✗|✗|✗|✗|✗|✗|✗|
|Year FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Grid (20*20) connectivity FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|

_Robust standard errors in parentheses clustered at the grid level. ∗p < ._ 10 _∗∗p < ._ 05 _∗∗∗ p <_ 0 _._ 01 _. We use a strategy that combines difference-in-differences and instrumental variables techniques. The causal effect of Internet is estimated using difference-in-differences, while the effects of the road and its interaction with the Internet are estimated using an instrumental variables approach. Our instruments are: the distance to the hypothetical road, the interaction between the distance to the hypothetical road and an indicator for whether an individual (or a firm) is connected to submarine Internet, lightning, and terrain elevation._ 60