|Mostert and Van<br>Heerden (2015)|South African<br>Provinces|NA|Levels|Railway|CGE|In the long run the building of the railway line will lead<br>to a 4.46 % increase in GDP, while aggregate<br>employment in will increase by 1.97 %|Hard to capture the<br>dynamic structure|
|---|---|---|---|---|---|---|---|
|Shi et al. (2017)|Chinese<br>municipalities|1990<br>to<br>2013|Y-Log<br>Difference<br>level; X-Log<br>levels|Railway, roads,<br>telphones, and<br>electricity<br>generation<br>capacity|VEC|Inverse U-shaped relationship between infrastructure<br>investment and growth posits a “crowding-out effect” of<br>private capital when infrastructure investment becomes<br>too dominant|Reverse causality|
|German-Soto and|Mexican|1985<br>|Y-Log<br>Difference|Roads|OLS|Urban area where major infrastructure provision exists,<br>higher rates of growth are also taking place. Estimated|Endogeneity; Reverse<br>li M|
|Bustillos (2014)|Urban Cities|to<br>2008|levels; X-<br>levels|Physical capital||output elasticity for roads is 0.2663* and is 0.0097** for<br>general physical capital|causaty; easurement<br>error|
|Banerjee et al.<br>(2012)|Chinese<br>Counties|1986<br>to<br>2003|Log<br>Difference<br>levels|Distance of<br>transportation lines|FE|Proximity to transportation networks have no effect on<br>per capita GDP growth|Endogeneity|
|Czernich et al.<br>(2011)|OECD<br>Countries|1996<br>to<br>2007|Log<br>Difference<br>levels|Broadband<br>penetration|IV|A 10-percentage point increase in broadband penetration<br>raised annual per capita growth by 0.9 to 1.5 percentage<br>points|External validity|
|**h Rate**<br>Calderón and<br>Servén (2010)|Sub-Saharan<br>African<br>Countries|1960<br>to<br>2005|Y-Log<br>Difference<br>Level; X-<br>Levels|Synthetic<br>Infrastructure<br>Index (telephone,<br>road, and<br>electricity)|GMM|Infrastructure development contributes to growth across<br>Africa. Estimated coefficient for the quantity index is<br>around 2|NA|
|**P Growt**<br>Herrerias (2010)|Chinese<br>National|1964<br>to<br>2004|Log<br>Difference<br>levels|Railway and<br>highway|VAR|Infrastructures have played a significant role in<br>accounting for long-run growth in China. Estimated<br>coefficient for railway is 0.08**|No cross-sectional<br>variation; Endogeneity|
|**GD**<br>Esfahani and<br>Ramırez (2003)|Cross<br>Countries|1965<br>to<br>1995|Cobb-<br>Douglas; Log<br>Difference<br>Level|Telephone<br>Power production<br>capacity|IV|Cross-country estimates of the model indicate that the<br>contribution of infrastructure services to GDP is<br>substantial. Estimated output elasticity is 0.0779*** for<br>the telephone and 0.1277*** for the power production<br>capacity|NA|
|Fernald (1999)|US Industries|1953<br>to<br>1989|Value-added<br>growth; First<br>Derivative;<br>Levels|Roads|SUR|When growth in roads changes, productivity growth<br>changes disproportionately in U.S. industries with more<br>vehicles. Cobb-Douglas coefficient is 0.35*|External validity|
|Sanchez (1998)|Latin<br>American<br>Countries<br>Cross-country|1970<br>to<br>1985<br>and<br>1980<br>to<br>1992<br>1971<br>to<br>1985|Log<br>Difference<br>levels; X-<br>Log Levels|Physical units<br>index|OLS|Indicators of investment in physical units of infrastructure<br>are positively and significantly correlated with growth in<br>two different samples of countries (Estimated coefficients<br>are 0.0031** and 0.0086**)|Endogeneity; Reverse<br>causality|

> # The dependent output variable is followed by the output to public capital stock/investment elasticity value, where p<0.01 is denoted by ***; p<0.05 is denoted by **; and p<0.1 is denoted by *****

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