Table A15: Heterogeneity in the Complementary Effect of Internet and Roads on Unskilled Employment

||(1)|(2)|(3)|(4)|(5)|(6)|(7)<br>**Low skilled**|(8)<br> **worker**|(9)|(10)|(11)|(12)|(13)|(14)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**All**<br>**Sample**|**Urban**<br>**Sample**|**Rural**<br>**Sample**|**Male**<br>**Sample**|**Female**<br>**Sample**|**Not educated**<br>**Sample**|**Low educated**<br>**Sample**<br>**(Primary)**|**Not more**<br>**than primary**<br>**Sample**|**higher**<br>**educated**<br>**Sample**|**20-34**<br>**groupe age**<br>**Sample**|**35 -44**<br>**groupe age**<br>**Sample**|**45-64**<br>**groupe age**<br>**Sample**|**More**<br>**developed**<br>**Sample**|**Less**<br>**developed**<br>**Sample**|
|Submarine internet|-0.131***|0.0779|-0.102***|-0.225***|-0.0888***|-0.211***|-0.0565**|-0.132***|0.00154|-0.129***|-0.133***|-0.134***|-0.243***|-0.0562**|
||(0.0195)|(0.0629)|(0.0244)|(0.0301)|(0.0213)|(0.0531)|(0.0217)|(0.0251)|(0.0203)|(0.0212)|(0.0243)|(0.0329)|(0.0606)|(0.0182)|
|Proximity to road (KM)|0.00524***<br>(0.000411)|0.00310***<br>(0.000839)|0.00479***<br>(0.000497)|0.000397<br>(0.000603)|0.00695***<br>(0.000482)|0.00409***<br>(0.000426)|0.00645***<br>(0.000915)|0.00541***<br>(0.000426)|0.000561<br>(0.000823)|0.00545***<br>(0.000437)|0.00530***<br>(0.000508)|0.00388***<br>(0.000604)|0.00520***<br>(0.000937)|0.00475***<br>(0.000443)|
|Submarine internet * road|-0.0194***|0.0455|-0.0114***|-0.0356***|-0.0121**|-0.0270**|-0.00622|-0.0162***|0.00241|-0.0196***|-0.0187***|-0.0192**|-0.0419**|-0.00476|
||(0.00422)|(0.0270)|(0.00316)|(0.00719)|(0.00439)|(0.00887)|(0.00381)|(0.00440)|(0.00578)|(0.00471)|(0.00479)|(0.00680)|(0.0144)|(0.00348)|
|N|724778|273255|451523|203643|521135|289648|189254|478902|245876|435086|193759|95933|277048|447730|
|ymean|0.529|0.640|0.462|0.659|0.473|0.408|0.495|0.443|0.692|0.567|0.469|0.476|0.583|0.495|
|r2|0.139|-0.0236|0.232|0.256|0.125|0.216|0.133|0.180|0.0676|0.123|0.143|0.202|0.0260|0.167|
|r~~2~~<br>a|0.139|-0.0238|0.232|0.256|0.125|0.216|0.133|0.180|0.0674|0.122|0.143|0.202|0.0259|0.167|
|Kleibergen-Paap F-statistic<br>_P > F_|81.24<br>0|25.13<br>0|68.91<br>0|59.07<br>0|85.55<br>0|73.85<br>0|52.38<br>0|76.55<br>0|54.16<br>0|79.46<br>0|78.55<br>0|50.98<br>0|21.96<br>0|74.50<br>0|
|Cragg-Donald Wald F-statistic|811.4|46.51|1091.3|249.6|559.5|208.3|332.6|576.7|211.6|427.3|271.2|112.7|116.3|1027.9|
|Windmeijer F-statistic|17.52|11.09|14.60|7.04|14.48|16.04|6.00|15.97|2.30|10.94|10.78|6.37|10.46|9.10|
|Kleibergen-Paap Wald F-statistic|19.18|4.987|15.81|13.23|20.38|17.55|11.37|18.18|11.46|18.62|18.63|11.27|4.448|17.62|
|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._ Table A16: Heterogeneity in the Complementary Effect of Internet and Roads on Services jobs

||(1)<br>**All**<br>**Sample**|(2)<br>**Urban**<br>**Sample**|(3)<br>**Rural**<br>**Sample**|(4)<br>**Male**<br>**Sample**|(5)<br>**Female**<br>**Sample**|(6)<br>**Not educated**<br>**Sample**|(7)<br>**Servi**<br>**Low educated**<br>**Sample**<br>**(Primary)**|(8)<br>**ces**<br>**Not more**<br>**than primary**<br>**Sample**|(9)<br>**higher**<br>**educated**<br>**Sample**|(10)<br>**20-34**<br>**groupe age**<br>**Sample**|(11)<br>**35 -44**<br>**groupe age**<br>**Sample**|(12)<br>**45-64**<br>**groupe age**<br>**Sample**|(13)<br>**More**<br>**developed**<br>**Sample**|(14)<br>**Less**<br>**developed**<br>**Sample**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Submarine internet|0.168***|0.195**|0.121***|0.207***|0.156***|0.235***|0.141***|0.198***|0.0979***|0.177***|0.155***|0.144***|0.273**|0.121***|
||(0.0236)|(0.0711)|(0.0207)|(0.0327)|(0.0227)|(0.0490)|(0.0245)|(0.0288)|(0.0259)|(0.0282)|(0.0242)|(0.0314)|(0.0839)|(0.0183)|
|Proximity to road (KM)|0.00126***<br>(0.000283)|0.00359***<br>(0.000862)|-0.000300<br>(0.000264)|0.00298***<br>(0.000522)|0.000664*<br>(0.000266)|0.000426<br>(0.000235)|0.00161**<br>(0.000554)|0.000718**<br>(0.000236)|0.00424***<br>(0.000969)|0.00140***<br>(0.000316)|0.00139***<br>(0.000342)|0.000627<br>(0.000505)|0.000622<br>(0.000806)|0.00131***<br>(0.000284)|
|Submarine internet * road|0.0294***|0.0627*|0.0124***|0.0348***|0.0268***|0.0331***|0.0217***|0.0284***|0.0198**|0.0331***|0.0233***|0.0262***|0.0603**|0.0172***|
||(0.00529)|(0.0307)|(0.00272)|(0.00772)|(0.00487)|(0.00815)|(0.00467)|(0.00521)|(0.00763)|(0.00651)|(0.00493)|(0.00655)|(0.0206)|(0.00370)|
|N|717891|278417|439474|197714|520177|281217|185596|466813|251078|431907|191991|93993|274836|443055|
|ymean|0.226|0.356|0.144|0.401|0.160|0.097|0.197|0.136|0.397|0.221|0.230|0.241|0.267|0.202|
|r2|0.0956|-0.00814|0.0935|0.205|0.0137|-0.0978|0.0789|0.00246|0.113|0.0595|0.156|0.166|-0.219|0.156|
|r~~2~~<br>~~a~~|0.0955|-0.00832|0.0934|0.204|0.0136|-0.0979|0.0787|0.00235|0.113|0.0594|0.156|0.166|-0.219|0.156|
|Kleibergen-Paap F-statistic|81.55|25.20|69.03|59.68|85.73|73.73|53.06|76.77|55.55|79.75|78.44|51.45|22.63|74.05|
|_P > F_|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|808.3|47.13|1065.5|246.9|559.2|205.3|327.8|567.1|216.4|426.7|269.3|111.8|118.4|1018.2|
|Windmeijer F-statistic|20.01|11.52|12.23|14.42|12.87|8.09|18.85|19.42|10.79|19.79|12.87|10.15|10.92|11.44|
|Kleibergen-Paap Wald F-statistic|19.23|5.006|15.81|13.30|20.42|17.50|11.52|18.19|11.77|18.69|18.52|11.38|4.581|17.48|
|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._