Table A13: Heterogeneity in the Complementary Effect of Internet and Roads on Skilled Employment

||(1)|(2)|(3)|(4)|(5)|(6)|(7)<br>**High skille**|(8)<br>**d 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|0.0962***|0.0807*|0.0612***|0.0669***|0.114***|0.152***|0.0517***|0.0970***|0.0516**|0.111***|0.0817***|0.0611**|0.200**|0.0620***|
||(0.0143)|(0.0359)|(0.0126)|(0.0176)|(0.0165)|(0.0324)|(0.0112)|(0.0149)|(0.0189)|(0.0175)|(0.0166)|(0.0230)|(0.0612)|(0.0111)|
|Proximity to road (KM)|0.0000988<br>(0.000176)|0.00128**<br>(0.000474)|-0.000410*<br>(0.000164)|0.000206<br>(0.000318)|0.0000295<br>(0.000180)|0.0000358<br>(0.000137)|-0.000593*<br>(0.000289)|-0.0000632<br>(0.000117)|-0.0000256<br>(0.000734)|0.000250<br>(0.000197)|-0.0000404<br>(0.000257)|-0.0000714<br>(0.000362)|-0.00107<br>(0.000556)|0.000377*<br>(0.000191)|
|Submarine Internet * road|0.0156***|0.0203|0.00609***|0.0115**|0.0183***|0.0218***|0.00735***|0.0137***|0.00924|0.0189***|0.0112***|0.0118*|0.0426**|0.00784***|
||(0.00314)|(0.0153)|(0.00160)|(0.00404)|(0.00349)|(0.00535)|(0.00198)|(0.00264)|(0.00543)|(0.00402)|(0.00320)|(0.00472)|(0.0148)|(0.00216)|
|N|767327|291558|475769|245304|522023|300363|199363|499726|267601|459291|203919|104117|300383|466944|
|ymean|0.121|0.209|0.067|0.166|0.100|0.034|0.070|0.048|0.257|0.115|0.129|0.133|0.140|0.109|
|r2|0.0471|0.0352|0.0449|0.0801|0.0114|-0.140|0.0332|-0.0176|0.0550|0.0174|0.0878|0.0960|-0.220|0.0810|
|r2<br>~~a~~|0.0471|0.0350|0.0448|0.0799|0.0113|-0.140|0.0329|-0.0177|0.0548|0.0173|0.0876|0.0956|-0.220|0.0810|
|Kleibergen-Paap F-statistic<br>_P > F_|81.15<br>0|25.38<br>0|68.70<br>0|58.25<br>0|85.42<br>0|73.52<br>0|51.83<br>0|75.86<br>0|56.02<br>0|79.25<br>0|78.44<br>0|51.29<br>0|22.25<br>0|74.15<br>0|
|Cragg-Donald Wald F-statistic|850.6|48.40|1142.5|290.5|559.9|213.6|346.5|595.8|229.0|447.1|282.4|120.4|124.6|1058.4|
|Windmeijer F-statistic|16.71|11.97|6.50|7.77|8.05|6.40|9.00|13.75|6.93|8.80|6.75|18.91|6.50|7.54|
|Kleibergen-Paap Wald F-statistic|19.14|5.036|15.73|13.06|20.34|17.41|11.21|17.93|11.91|18.57|18.57|11.31|4.513|17.52|
|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 A14: Heterogeneity in the Complementary Effect of Internet and Roads on Skilled jobs

||(1)|(2)|(3)|(4)|(5)|(6)|(7)<br>**Skilled w**|(8)<br>**orker**|(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|0.129***|0.138*|0.0991***|0.171***|0.109***|0.199***|0.0862***|0.146***|0.0607**|0.133***|0.123***|0.116***|0.230**|0.100***|
||(0.0189)|(0.0571)|(0.0179)|(0.0341)|(0.0161)|(0.0419)|(0.0185)|(0.0226)|(0.0214)|(0.0218)|(0.0209)|(0.0301)|(0.0715)|(0.0155)|
|Proximity to road (KM)|0.000939***|0.00296***|-0.000293|0.00286***|0.000141|0.000338|0.00146**|0.000484*|0.00234**|0.00106***|0.000929**|0.000699|-0.0000496|0.00114***|
||(0.000233)|(0.000676)|(0.000230)|(0.000477)|(0.000216)|(0.000197)|(0.000474)|(0.000192)|(0.000801)|(0.000261)|(0.000300)|(0.000456)|(0.000664)|(0.000251)|
|Submarine internet * road|0.0218***|0.0429|0.00985***|0.0352***|0.0165***|0.0280***|0.0139***|0.0211***|0.0128*|0.0234***|0.0185***|0.0224***|0.0495**|0.0131***|
||(0.00417)|(0.0244)|(0.00230)|(0.00818)|(0.00336)|(0.00690)|(0.00347)|(0.00405)|(0.00619)|(0.00495)|(0.00418)|(0.00624)|(0.0172)|(0.00310)|
|N|752131|286213|465918|244272|507859|295085|195580|490665|261466|448254|201003|102874|292000|460131|
|ymean|0.187|0.297|0.121|0.295|0.136|0.085|0.155|0.113|0.329|0.184|0.194|0.194|0.216|0.170|
|r2|0.0734|0.0278|0.0589|0.0533|0.0430|-0.0758|0.0703|0.00778|0.0793|0.0561|0.115|0.108|-0.170|0.122|
|r2<br>~~a~~|0.0733|0.0276|0.0588|0.0531|0.0429|-0.0760|0.0700|0.00768|0.0791|0.0560|0.115|0.108|-0.170|0.122|
|Kleibergen-Paap F-statistic|80.89|23.87|70.07|58.19|85.11|74.85|52.47|76.84|54.43|78.63|77.85|51.02|21.37|74.14|
|_P > F_|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|885.1|45.01|1219.3|288.8|596.1|247.4|347.0|632.1|229.5|471.4|289.5|121.8|134.8|1043.2|
|Windmeijer F-statistic|26.87|8.43|16.15|14.95|19.81|21.51|7.26|18.00|3.20|15.77|11.16|5.67|7.95|13.65|
|Kleibergen-Paap Wald F-statistic|18.99|4.748|16.04|13.04|20.12|17.78|11.34|18.10|11.56|18.33|18.39|11.24|4.320|17.49|
|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._