Table A8: Heterogeneity in the Joint Effect of Internet and Roads on Employment: Place of Residence

||(1)|(2)<br>**Worked**<br>**past **|(3)<br> **during**<br> **year**|(4)|(5)|(6)<br>**Curr**<br>**wor**|(7)<br>**ently**<br>**king**|(8)|(9)|(10)|(11)<br>**Ho**|(12)<br>**urs worked**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Urban**<br>**Sample**|**Rural**<br>**Sample**|**high**<br>**developed**<br>**Sample**<br>**D**|**Low**<br>**developed**<br>**Sample**<br>**HS**|**Urban**<br>**Sample**|**Rural**<br>**Sample**|**high**<br>**developed**<br>**Sample**|**Low**<br>**developed**<br>**Sample**|**Urban**<br>**Sample**|**Rural**<br>**Sample**<br>|**high**<br>**developed**<br>**Sample**<br>**LSMS**|**Low**<br>**developed**<br>**Sample**|
|Submarine internet|0.210*|0.0369|0.0891|0.0603**|0.190*|0.0165|0.0419|0.0514*|2.005|-0.390**|-0.0893|-1.002***|
||(0.101)|(0.0239)|(0.0516)|(0.0209)|(0.0920)|(0.0240)|(0.0443)|(0.0209)|(2.464)|(0.189)|(0.0590)|(0.349)|
|Proximity to road (KM)|0.00622***|0.00822***|0.00962***|0.00689***|0.00709***|0.00758***|0.00938***|0.00725***|4.782|0.327|-20.84|1.188***|
||(0.00118)|(0.000613)|(0.00101)|(0.000515)|(0.00119)|(0.000581)|(0.00104)|(0.000509)|(8.135)|(0.320)|(50.56)|(0.341)|
|Submarine internet * road|0.0905*|0.00228|0.0152|0.0122**|0.0797*|-0.000414|0.00702|0.00926*|4.691|3.246|-15.84|6.745***|
||(0.0442)|(0.00312)|(0.0123)|(0.00425)|(0.0400)|(0.00306)|(0.0104)|(0.00416)|(8.322)|(2.920)|(43.33)|(2.356)|
|N|287502|468833|295535|460800|265370|431047|263715|432702|31001|440181|285733|185449|
|ymean|0.734|0.752|0.764|0.733|0.682|0.696|0.671|0.671|4.709|0.750|4.981|1.295|
|r2|-0.318|0.0754|0.0151|0.0675|-0.239|0.117|0.0767|0.0816|-1.010|-0.408|-0.0356|-0.817|
|r2<br>~~a~~|-0.318|0.0753|0.0150|0.0675|-0.240|0.117|0.0767|0.0816|-1.011|-0.408|-0.0356|-0.817|
|Kleibergen-Paap F-statistic|25.81|67.77|21.60|73.31|24.89|69.03|22.38|74.27|4.116|10.04|2.660|5.403|
|_P > F_|0|0|0|0|0|0|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|47.89|1146.8|125.3|1050.5|45.69|1048.4|114.9|1002.2|57.64|1064.3|62.03|363.2|
|Windmeijer F-statistic|12.11|16.02|29.89|32.08|13.57|12.04|11.84|16.73|4.12|10.04|12.66|15.40|
|Kleibergen-Paap Wald F-statistic|5.120|15.50|4.382|17.27|4.940|15.79|4.532|17.54|1.226|1.983|0.555|3.652|
|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._